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graph data model (EGDM)</title> <meta name="description" content="Search results for: elemental graph data model (EGDM)"> <meta name="keywords" content="elemental graph data model (EGDM)"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="elemental graph data model (EGDM)"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 36079</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: elemental graph data model (EGDM)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36079</span> Elemental Graph Data Model: A Semantic and Topological Representation of Building Elements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasmeen%20A.%20S.%20Essawy">Yasmeen A. S. Essawy</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Nassar"> Khaled Nassar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20information%20modeling%20%28BIM%29" title="building information modeling (BIM)">building information modeling (BIM)</a>, <a href="https://publications.waset.org/abstracts/search?q=elemental%20graph%20data%20model%20%28EGDM%29" title=" elemental graph data model (EGDM)"> elemental graph data model (EGDM)</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20and%20topological%20data%20models" title=" geometric and topological data models"> geometric and topological data models</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20theory" title=" graph theory"> graph theory</a> </p> <a href="https://publications.waset.org/abstracts/70542/elemental-graph-data-model-a-semantic-and-topological-representation-of-building-elements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70542.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36078</span> Efficient Filtering of Graph Based Data Using Graph Partitioning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nileshkumar%20Vaishnav">Nileshkumar Vaishnav</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Tatu"> Aditya Tatu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An algebraic framework for processing graph signals axiomatically designates the graph adjacency matrix as the shift operator. In this setup, we often encounter a problem wherein we know the filtered output and the filter coefficients, and need to find out the input graph signal. Solution to this problem using direct approach requires O(N3) operations, where N is the number of vertices in graph. In this paper, we adapt the spectral graph partitioning method for partitioning of graphs and use it to reduce the computational cost of the filtering problem. We use the example of denoising of the temperature data to illustrate the efficacy of the approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20signal%20processing" title="graph signal processing">graph signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20partitioning" title=" graph partitioning"> graph partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20filtering%20on%20graphs" title=" inverse filtering on graphs"> inverse filtering on graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=algebraic%20signal%20processing" title=" algebraic signal processing"> algebraic signal processing</a> </p> <a href="https://publications.waset.org/abstracts/59397/efficient-filtering-of-graph-based-data-using-graph-partitioning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59397.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36077</span> Graph Similarity: Algebraic Model and Its Application to Nonuniform Signal Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nileshkumar%20Vishnav">Nileshkumar Vishnav</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Tatu"> Aditya Tatu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A recent approach of representing graph signals and graph filters as polynomials is useful for graph signal processing. In this approach, the adjacency matrix plays pivotal role; instead of the more common approach involving graph-Laplacian. In this work, we follow the adjacency matrix based approach and corresponding algebraic signal model. We further expand the theory and introduce the concept of similarity of two graphs. The similarity of graphs is useful in that key properties (such as filter-response, algebra related to graph) get transferred from one graph to another. We demonstrate potential applications of the relation between two similar graphs, such as nonuniform filter design, DTMF detection and signal reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20signal%20processing" title="graph signal processing">graph signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=algebraic%20signal%20processing" title=" algebraic signal processing"> algebraic signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20similarity" title=" graph similarity"> graph similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=isospectral%20graphs" title=" isospectral graphs"> isospectral graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=nonuniform%20signal%20processing" title=" nonuniform signal processing"> nonuniform signal processing</a> </p> <a href="https://publications.waset.org/abstracts/59404/graph-similarity-algebraic-model-and-its-application-to-nonuniform-signal-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36076</span> Explainable Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Pham">David Pham</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongfeng%20Zhang"> Yongfeng Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=explainable%20AI" title="explainable AI">explainable AI</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20network" title=" graph neural network"> graph neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=node%20classification" title=" node classification"> node classification</a> </p> <a href="https://publications.waset.org/abstracts/156796/explainable-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156796.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36075</span> Speedup Breadth-First Search by Graph Ordering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiuyi%20Lyu">Qiuyi Lyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Gong"> Bin Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breadth-First Search(BFS) is a core graph algorithm that is widely used for graph analysis. As it is frequently used in many graph applications, improve the BFS performance is essential. In this paper, we present a graph ordering method that could reorder the graph nodes to achieve better data locality, thus, improving the BFS performance. Our method is based on an observation that the sibling relationships will dominate the cache access pattern during the BFS traversal. Therefore, we propose a frequency-based model to construct the graph order. First, we optimize the graph order according to the nodes’ visit frequency. Nodes with high visit frequency will be processed in priority. Second, we try to maximize the child nodes overlap layer by layer. As it is proved to be NP-hard, we propose a heuristic method that could greatly reduce the preprocessing overheads. We conduct extensive experiments on 16 real-world datasets. The result shows that our method could achieve comparable performance with the state-of-the-art methods while the graph ordering overheads are only about 1/15. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breadth-first%20search" title="breadth-first search">breadth-first search</a>, <a href="https://publications.waset.org/abstracts/search?q=BFS" title=" BFS"> BFS</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20ordering" title=" graph ordering"> graph ordering</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20algorithm" title=" graph algorithm"> graph algorithm</a> </p> <a href="https://publications.waset.org/abstracts/136790/speedup-breadth-first-search-by-graph-ordering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36074</span> Topological Indices of Some Graph Operations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Mary">U. Mary </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Let be a graph with a finite, nonempty set of objects called vertices together with a set of unordered pairs of distinct vertices of called edges. The vertex set is denoted by and the edge set by. Given two graphs and the wiener index of, wiener index for the splitting graph of a graph, the first Zagreb index of and its splitting graph, the 3-steiner wiener index of, the 3-steiner wiener index of a special graph are explored in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complementary%20prism%20graph" title="complementary prism graph">complementary prism graph</a>, <a href="https://publications.waset.org/abstracts/search?q=first%20Zagreb%20index" title=" first Zagreb index"> first Zagreb index</a>, <a href="https://publications.waset.org/abstracts/search?q=neighborhood%20corona%20graph" title=" neighborhood corona graph"> neighborhood corona graph</a>, <a href="https://publications.waset.org/abstracts/search?q=steiner%20distance" title=" steiner distance"> steiner distance</a>, <a href="https://publications.waset.org/abstracts/search?q=splitting%20graph" title=" splitting graph"> splitting graph</a>, <a href="https://publications.waset.org/abstracts/search?q=steiner%20wiener%20index" title=" steiner wiener index"> steiner wiener index</a>, <a href="https://publications.waset.org/abstracts/search?q=wiener%20index" title=" wiener index"> wiener index</a> </p> <a href="https://publications.waset.org/abstracts/16774/topological-indices-of-some-graph-operations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16774.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36073</span> QoS-CBMG: A Model for e-Commerce Customer Behavior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Ghavamipoor">Hoda Ghavamipoor</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Alireza%20Hashemi%20Golpayegani"> S. Alireza Hashemi Golpayegani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach to model the customer interaction with e-commerce websites is presented. Considering the service quality level as a predictive feature, we offer an improved method based on the Customer Behavior Model Graph (CBMG), a state-transition graph model. To derive the Quality of Service sensitive-CBMG (QoS-CBMG) model, process-mining techniques is applied to pre-processed website server logs which are categorized as ‘buy’ or ‘visit’. Experimental results on an e-commerce website data confirmed that the proposed method outperforms CBMG based method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20behavior%20model" title="customer behavior model">customer behavior model</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20commerce" title=" electronic commerce"> electronic commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20service" title=" quality of service"> quality of service</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20behavior%20model%20graph" title=" customer behavior model graph"> customer behavior model graph</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a> </p> <a href="https://publications.waset.org/abstracts/41945/qos-cbmg-a-model-for-e-commerce-customer-behavior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36072</span> A Graph-Based Retrieval Model for Passage Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Junjie%20Zhong">Junjie Zhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Hong"> Kai Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Lei%20Wang"> Lei Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Passage Retrieval (PR) plays an important role in many Natural Language Processing (NLP) tasks. Traditional efficient retrieval models relying on exact term-matching, such as TF-IDF or BM25, have nowadays been exceeded by pre-trained language models which match by semantics. Though they gain effectiveness, deep language models often require large memory as well as time cost. To tackle the trade-off between efficiency and effectiveness in PR, this paper proposes Graph Passage Retriever (GraphPR), a graph-based model inspired by the development of graph learning techniques. Different from existing works, GraphPR is end-to-end and integrates both term-matching information and semantics. GraphPR constructs a passage-level graph from BM25 retrieval results and trains a GCN-like model on the graph with graph-based objectives. Passages were regarded as nodes in the constructed graph and were embedded in dense vectors. PR can then be implemented using embeddings and a fast vector-similarity search. Experiments on a variety of real-world retrieval datasets show that the proposed model outperforms related models in several evaluation metrics (e.g., mean reciprocal rank, accuracy, F1-scores) while maintaining a relatively low query latency and memory usage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=efficiency" title="efficiency">efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=effectiveness" title=" effectiveness"> effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20learning" title=" graph learning"> graph learning</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</a>, <a href="https://publications.waset.org/abstracts/search?q=passage%20retrieval" title=" passage retrieval"> passage retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=term-matching%20model" title=" term-matching model"> term-matching model</a> </p> <a href="https://publications.waset.org/abstracts/162229/a-graph-based-retrieval-model-for-passage-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162229.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36071</span> Survey Paper on Graph Coloring Problem and Its Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Chharia">Prateek Chharia</a>, <a href="https://publications.waset.org/abstracts/search?q=Biswa%20Bhusan%20Ghosh"> Biswa Bhusan Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graph coloring is one of the prominent concepts in graph coloring. It can be defined as a coloring of the various regions of the graph such that all the constraints are fulfilled. In this paper various graphs coloring approaches like greedy coloring, Heuristic search for maximum independent set and graph coloring using edge table is described. Graph coloring can be used in various real time applications like student time tabling generation, Sudoku as a graph coloring problem, GSM phone network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20coloring" title="graph coloring">graph coloring</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20coloring" title=" greedy coloring"> greedy coloring</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20search" title=" heuristic search"> heuristic search</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20table" title=" edge table"> edge table</a>, <a href="https://publications.waset.org/abstracts/search?q=sudoku%20as%20a%20graph%20coloring%20problem" title=" sudoku as a graph coloring problem"> sudoku as a graph coloring problem</a> </p> <a href="https://publications.waset.org/abstracts/19691/survey-paper-on-graph-coloring-problem-and-its-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19691.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">540</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36070</span> A New Graph Theoretic Problem with Ample Practical Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Hakan%20Karaata">Mehmet Hakan Karaata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm" title="algorithm">algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=cycle" title=" cycle"> cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20algorithm" title=" graph algorithm"> graph algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20theory" title=" graph theory"> graph theory</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20structuring" title=" network structuring"> network structuring</a> </p> <a href="https://publications.waset.org/abstracts/67285/a-new-graph-theoretic-problem-with-ample-practical-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67285.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36069</span> Multi-Stream Graph Attention Network for Recommendation with Knowledge Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhifei%20Hu">Zhifei Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Xia"> Feng Xia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title="graph attention network">graph attention network</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title=" knowledge graph"> knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation" title=" recommendation"> recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20propagation" title=" information propagation"> information propagation</a> </p> <a href="https://publications.waset.org/abstracts/150710/multi-stream-graph-attention-network-for-recommendation-with-knowledge-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150710.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">117</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36068</span> Complete Tripartite Graphs with Spanning Maximal Planar Subgraphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Severino%20Gervacio">Severino Gervacio</a>, <a href="https://publications.waset.org/abstracts/search?q=Velimor%20Almonte"> Velimor Almonte</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Natalio"> Emmanuel Natalio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simple graph is planar if it there is a way of drawing it in the plane without edge crossings. A planar graph which is not a proper spanning subgraph of another planar graph is a maximal planar graph. We prove that for complete tripartite graphs of order at most 9, the only ones that contain a spanning maximal planar subgraph are K1,1,1, K2,2,2, K2,3,3, and K3,3,3. The main result gives a necessary and sufficient condition for the complete tripartite graph Kx,y,z to contain a spanning maximal planar subgraph. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complete%20tripartite%20graph" title="complete tripartite graph">complete tripartite graph</a>, <a href="https://publications.waset.org/abstracts/search?q=graph" title=" graph"> graph</a>, <a href="https://publications.waset.org/abstracts/search?q=maximal%20planar%20graph" title=" maximal planar graph"> maximal planar graph</a>, <a href="https://publications.waset.org/abstracts/search?q=planar%20graph" title=" planar graph"> planar graph</a>, <a href="https://publications.waset.org/abstracts/search?q=subgraph" title=" subgraph"> subgraph</a> </p> <a href="https://publications.waset.org/abstracts/59157/complete-tripartite-graphs-with-spanning-maximal-planar-subgraphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59157.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36067</span> Graph Codes - 2D Projections of Multimedia Feature Graphs for Fast and Effective Retrieval</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Wagenpfeil">Stefan Wagenpfeil</a>, <a href="https://publications.waset.org/abstracts/search?q=Felix%20Engel"> Felix Engel</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20McKevitt"> Paul McKevitt</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20Hemmje"> Matthias Hemmje</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multimedia Indexing and Retrieval is generally designed and implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results but also leads to more complex graph structures. However, graph-traversal-based algorithms for similarity are quite inefficient and computation intensive, especially for large data structures. To deliver fast and effective retrieval, an efficient similarity algorithm, particularly for large graphs, is mandatory. Hence, in this paper, we define a graph-projection into a 2D space (Graph Code) as well as the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph-traversals due to a simpler processing model and a high level of parallelization. In consequence, we prove that the effectiveness of retrieval also increases substantially, as Graph Codes facilitate more levels of detail in feature fusion. Thus, Graph Codes provide a significant increase in efficiency and effectiveness (especially for Multimedia indexing and retrieval) and can be applied to images, videos, audio, and text information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indexing" title="indexing">indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval" title=" retrieval"> retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=multimedia" title=" multimedia"> multimedia</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20algorithm" title=" graph algorithm"> graph algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20code" title=" graph code"> graph code</a> </p> <a href="https://publications.waset.org/abstracts/135289/graph-codes-2d-projections-of-multimedia-feature-graphs-for-fast-and-effective-retrieval" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36066</span> Neighborhood Graph-Optimized Preserving Discriminant Analysis for Image Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoheng%20Tan">Xiaoheng Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianfang%20Li"> Xianfang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Guo"> Tan Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuchuan%20Liu"> Yuchuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhijun%20Yang"> Zhijun Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongye%20Li"> Hongye Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Fu"> Kai Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yufang%20Wu"> Yufang Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Heling%20Gong"> Heling Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20optimization%20local%20preserving%20projection" title=" graph optimization local preserving projection"> graph optimization local preserving projection</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=L%E2%82%82" title=" L₂"> L₂</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%82%81%20norm" title="₁ norm">₁ norm</a> </p> <a href="https://publications.waset.org/abstracts/129863/neighborhood-graph-optimized-preserving-discriminant-analysis-for-image-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36065</span> Enhancing Knowledge Graph Convolutional Networks with Structural Adaptive Receptive Fields for Improved Node Representation and Information Aggregation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zheng%20Zhihao">Zheng Zhihao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title="knowledge graph">knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20networks" title=" graph neural networks"> graph neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20adaptive%20receptive%20fields" title=" structural adaptive receptive fields"> structural adaptive receptive fields</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20aggregation" title=" information aggregation"> information aggregation</a> </p> <a href="https://publications.waset.org/abstracts/191048/enhancing-knowledge-graph-convolutional-networks-with-structural-adaptive-receptive-fields-for-improved-node-representation-and-information-aggregation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191048.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">34</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36064</span> A Summary-Based Text Classification Model for Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Liu">Shuo Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20natural%20language%20processing" title="Chinese natural language processing">Chinese natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=abstract%20extraction" title=" abstract extraction"> abstract extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a> </p> <a href="https://publications.waset.org/abstracts/158060/a-summary-based-text-classification-model-for-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158060.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36063</span> Analyzing the Factors that Cause Parallel Performance Degradation in Parallel Graph-Based Computations Using Graph500</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Elfituri">Mustafa Elfituri</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Cook"> Jonathan Cook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, graph-based computations have become more important in large-scale scientific computing as they can provide a methodology to model many types of relations between independent objects. They are being actively used in fields as varied as biology, social networks, cybersecurity, and computer networks. At the same time, graph problems have some properties such as irregularity and poor locality that make their performance different than regular applications performance. Therefore, parallelizing graph algorithms is a hard and challenging task. Initial evidence is that standard computer architectures do not perform very well on graph algorithms. Little is known exactly what causes this. The Graph500 benchmark is a representative application for parallel graph-based computations, which have highly irregular data access and are driven more by traversing connected data than by computation. In this paper, we present results from analyzing the performance of various example implementations of Graph500, including a shared memory (OpenMP) version, a distributed (MPI) version, and a hybrid version. We measured and analyzed all the factors that affect its performance in order to identify possible changes that would improve its performance. Results are discussed in relation to what factors contribute to performance degradation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20computation" title="graph computation">graph computation</a>, <a href="https://publications.waset.org/abstracts/search?q=graph500%20benchmark" title=" graph500 benchmark"> graph500 benchmark</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20architectures" title=" parallel architectures"> parallel architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20programming" title=" parallel programming"> parallel programming</a>, <a href="https://publications.waset.org/abstracts/search?q=workload%20characterization." title=" workload characterization."> workload characterization.</a> </p> <a href="https://publications.waset.org/abstracts/133666/analyzing-the-factors-that-cause-parallel-performance-degradation-in-parallel-graph-based-computations-using-graph500" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36062</span> Improvement a Lower Bound of Energy for Some Family of Graphs, Related to Determinant of Adjacency Matrix</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saieed%20%20Akbari">Saieed Akbari</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Bagheri"> Yousef Bagheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Hossein%20Ghodrati"> Amir Hossein Ghodrati</a>, <a href="https://publications.waset.org/abstracts/search?q=Sima%20Saadat%20Akhtar"> Sima Saadat Akhtar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Let G be a simple graph with the vertex set V (G) and with the adjacency matrix A (G). The energy E (G) of G is defined to be the sum of the absolute values of all eigenvalues of A (G). Also let n and m be number of edges and vertices of the graph respectively. A regular graph is a graph where each vertex has the same number of neighbours. Given a graph G, its line graph L(G) is a graph such that each vertex of L(G) represents an edge of G; and two vertices of L(G) are adjacent if and only if their corresponding edges share a common endpoint in G. In this paper we show that for every regular graphs and also for every line graphs such that (G) 3 we have, E(G) 2nm + n 1. Also at the other part of the paper we prove that 2 (G) E(G) for an arbitrary graph G. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eigenvalues" title="eigenvalues">eigenvalues</a>, <a href="https://publications.waset.org/abstracts/search?q=energy" title=" energy"> energy</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20graphs" title=" line graphs"> line graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20number" title=" matching number"> matching number</a> </p> <a href="https://publications.waset.org/abstracts/99652/improvement-a-lower-bound-of-energy-for-some-family-of-graphs-related-to-determinant-of-adjacency-matrix" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99652.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36061</span> Graph Neural Network-Based Classification for Disease Prediction in Health Care Heterogeneous Data Structures of Electronic Health Record</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raghavi%20C.%20Janaswamy">Raghavi C. Janaswamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronic%20health%20record" title="electronic health record">electronic health record</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20network" title=" graph neural network"> graph neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20data" title=" heterogeneous data"> heterogeneous data</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/157840/graph-neural-network-based-classification-for-disease-prediction-in-health-care-heterogeneous-data-structures-of-electronic-health-record" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157840.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36060</span> Characterising Stable Model by Extended Labelled Dependency Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asraful%20Islam">Asraful Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extended dependency graph (EDG) is a state-of-the-art isomorphic graph to represent normal logic programs (NLPs) that can characterize the consistency of NLPs by graph analysis. To construct the vertices and arcs of an EDG, additional renaming atoms and rules besides those the given program provides are used, resulting in higher space complexity compared to the corresponding traditional dependency graph (TDG). In this article, we propose an extended labeled dependency graph (ELDG) to represent an NLP that shares an equal number of nodes and arcs with TDG and prove that it is isomorphic to the domain program. The number of nodes and arcs used in the underlying dependency graphs are formulated to compare the space complexity. Results show that ELDG uses less memory to store nodes, arcs, and cycles compared to EDG. To exhibit the desirability of ELDG, firstly, the stable models of the kernel form of NLP are characterized by the admissible coloring of ELDG; secondly, a relation of the stable models of a kernel program with the handles of the minimal, odd cycles appearing in the corresponding ELDG has been established; thirdly, to our best knowledge, for the first time an inverse transformation from a dependency graph to the representing NLP w.r.t. ELDG has been defined that enables transferring analytical results from the graph to the program straightforwardly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=normal%20logic%20program" title="normal logic program">normal logic program</a>, <a href="https://publications.waset.org/abstracts/search?q=isomorphism%20of%20graph" title=" isomorphism of graph"> isomorphism of graph</a>, <a href="https://publications.waset.org/abstracts/search?q=extended%20labelled%20dependency%20graph" title=" extended labelled dependency graph"> extended labelled dependency graph</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20graph%20transforma-tion" title=" inverse graph transforma-tion"> inverse graph transforma-tion</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20colouring" title=" graph colouring"> graph colouring</a> </p> <a href="https://publications.waset.org/abstracts/137606/characterising-stable-model-by-extended-labelled-dependency-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137606.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36059</span> Recommender System Based on Mining Graph Databases for Data-Intensive Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Gamal">Mostafa Gamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20K.%20Mohamed"> Hoda K. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Islam%20El-Maddah"> Islam El-Maddah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Hamdi"> Ali Hamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20databases" title="graph databases">graph databases</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20systems" title=" recommendation systems"> recommendation systems</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20metrics" title=" similarity metrics"> similarity metrics</a> </p> <a href="https://publications.waset.org/abstracts/163018/recommender-system-based-on-mining-graph-databases-for-data-intensive-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163018.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36058</span> Metric Dimension on Line Graph of Honeycomb Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Hussain">M. Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Aqsa%20Farooq"> Aqsa Farooq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Let G = (V,E) be a connected graph and distance between any two vertices a and b in G is a−b geodesic and is denoted by d(a, b). A set of vertices W resolves a graph G if each vertex is uniquely determined by its vector of distances to the vertices in W. A metric dimension of G is the minimum cardinality of a resolving set of G. In this paper line graph of honeycomb network has been derived and then we calculated the metric dimension on line graph of honeycomb network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Resolving%20set" title="Resolving set">Resolving set</a>, <a href="https://publications.waset.org/abstracts/search?q=Metric%20dimension" title=" Metric dimension"> Metric dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=Honeycomb%20network" title=" Honeycomb network"> Honeycomb network</a>, <a href="https://publications.waset.org/abstracts/search?q=Line%20graph" title=" Line graph"> Line graph</a> </p> <a href="https://publications.waset.org/abstracts/101558/metric-dimension-on-line-graph-of-honeycomb-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101558.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36057</span> A Novel Computer-Generated Hologram (CGH) Achieved Scheme Generated from Point Cloud by Using a Lens Array</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei-Na%20Li">Wei-Na Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Mei-Lan%20Piao"> Mei-Lan Piao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nam%20Kim"> Nam Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We proposed a novel computer-generated hologram (CGH) achieved scheme, wherein the CGH is generated from a point cloud which is transformed by a mapping relationship of a series of elemental images captured from a real three-dimensional (3D) object by using a lens array. This scheme is composed of three procedures: mapping from elemental images to point cloud, hologram generation, and hologram display. A mapping method is figured out to achieve a virtual volume date (point cloud) from a series of elemental images. This mapping method consists of two steps. Firstly, the coordinate (x, y) pairs and its appearing number are calculated from the series of sub-images, which are generated from the elemental images. Secondly, a series of corresponding coordinates (x, y, z) are calculated from the elemental images. Then a hologram is generated from the volume data that is calculated by the previous two steps. Eventually, a spatial light modulator (SLM) and a green laser beam are utilized to display this hologram and reconstruct the original 3D object. In this paper, in order to show a more auto stereoscopic display of a real 3D object, we successfully obtained the actual depth data of every discrete point of the real 3D object, and overcame the inherent drawbacks of the depth camera by obtaining point cloud from the elemental images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elemental%20image" title="elemental image">elemental image</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20cloud" title=" point cloud"> point cloud</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-generated%20hologram%20%28CGH%29" title=" computer-generated hologram (CGH)"> computer-generated hologram (CGH)</a>, <a href="https://publications.waset.org/abstracts/search?q=autostereoscopic%20display" title=" autostereoscopic display"> autostereoscopic display</a> </p> <a href="https://publications.waset.org/abstracts/11827/a-novel-computer-generated-hologram-cgh-achieved-scheme-generated-from-point-cloud-by-using-a-lens-array" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11827.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">584</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36056</span> A Study of Families of Bistar and Corona Product of Graph: Reverse Topological Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gowtham%20Kalkere%20Jayanna">Gowtham Kalkere Jayanna</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Nazri%20Husin"> Mohamad Nazri Husin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graph theory, chemistry, and technology are all combined in cheminformatics. The structure and physiochemical properties of organic substances are linked using some useful graph invariants and the corresponding molecular graph. In this paper, we study specific reverse topological indices such as the reverse sum-connectivity index, the reverse Zagreb index, the reverse arithmetic-geometric, and the geometric-arithmetic, the reverse Sombor, the reverse Nirmala indices for the bistar graphs B (n: m) and the corona product Kₘ∘Kₙ', where Kₙ' Represent the complement of a complete graph Kₙ. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reverse%20topological%20indices" title="reverse topological indices">reverse topological indices</a>, <a href="https://publications.waset.org/abstracts/search?q=bistar%20graph" title=" bistar graph"> bistar graph</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20corona%20product" title=" the corona product"> the corona product</a>, <a href="https://publications.waset.org/abstracts/search?q=graph" title=" graph"> graph</a> </p> <a href="https://publications.waset.org/abstracts/166540/a-study-of-families-of-bistar-and-corona-product-of-graph-reverse-topological-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166540.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36055</span> A Graph Library Development Based on the Service-‎Oriented Architecture: Used for Representation of the ‎Biological ‎Systems in the Computer Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrshad%20Khosraviani">Mehrshad Khosraviani</a>, <a href="https://publications.waset.org/abstracts/search?q=Sepehr%20Najjarpour"> Sepehr Najjarpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considering the usage of graph-based approaches in systems and synthetic biology, and the various types of ‎the graphs employed by them, a comprehensive graph library based ‎on the three-tier architecture (3TA) was previously introduced for full representation of the biological systems. Although proposing a 3TA-based graph library, three following reasons motivated us to redesign the graph ‎library based on the service-oriented architecture (SOA): (1) Maintaining the accuracy of the data related to an input graph (including its edges, its ‎vertices, its topology, etc.) without involving the end user:‎ Since, in the case of using 3TA, the library files are available to the end users, they may ‎be utilized incorrectly, and consequently, the invalid graph data will be provided to the ‎computer algorithms. However, considering the usage of the SOA, the operation of the ‎graph registration is specified as a service by encapsulation of the library files. In other words, overall control operations needed for registration of the valid data will be the ‎responsibility of the services. (2) Partitioning of the library product into some different parts: Considering 3TA, a whole library product was provided in general. While here, the product ‎can be divided into smaller ones, such as an AND/OR graph drawing service, and each ‎one can be provided individually. As a result, the end user will be able to select any ‎parts of the library product, instead of all features, to add it to a project. (3) Reduction of the complexities: While using 3TA, several other libraries must be needed to add for connecting to the ‎database, responsibility of the provision of the needed library resources in the SOA-‎based graph library is entrusted with the services by themselves. Therefore, the end user ‎who wants to use the graph library is not involved with its complexity. In the end, in order to ‎make ‎the library easier to control in the system, and to restrict the end user from accessing the files, ‎it was preferred to use the service-oriented ‎architecture ‎‎(SOA) over the three-tier architecture (3TA) and to redevelop the previously proposed graph library based on it‎. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bio-Design%20Automation" title="Bio-Design Automation">Bio-Design Automation</a>, <a href="https://publications.waset.org/abstracts/search?q=Biological%20System" title=" Biological System"> Biological System</a>, <a href="https://publications.waset.org/abstracts/search?q=Graph%20Library" title=" Graph Library"> Graph Library</a>, <a href="https://publications.waset.org/abstracts/search?q=Service-Oriented%20Architecture" title=" Service-Oriented Architecture"> Service-Oriented Architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=Systems%20and%20Synthetic%20Biology" title=" Systems and Synthetic Biology"> Systems and Synthetic Biology</a> </p> <a href="https://publications.waset.org/abstracts/85620/a-graph-library-development-based-on-the-service-oriented-architecture-used-for-representation-of-the-biological-systems-in-the-computer-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85620.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36054</span> On the Zeros of the Degree Polynomial of a Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20R.%20Nayaka">S. R. Nayaka</a>, <a href="https://publications.waset.org/abstracts/search?q=Putta%20Swamy"> Putta Swamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graph polynomial is one of the algebraic representations of the Graph. The degree polynomial is one of the simple algebraic representations of graphs. The degree polynomial of a graph G of order n is the polynomial Deg(G, x) with the coefficients deg(G,i) where deg(G,i) denotes the number of vertices of degree i in G. In this article, we investigate the behavior of the roots of some families of Graphs in the complex field. We investigate for the graphs having only integral roots. Further, we characterize the graphs having single roots or having real roots and behavior of the polynomial at the particular value is also obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=degree%20polynomial" title="degree polynomial">degree polynomial</a>, <a href="https://publications.waset.org/abstracts/search?q=regular%20graph" title=" regular graph"> regular graph</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20and%20maximum%20degree" title=" minimum and maximum degree"> minimum and maximum degree</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20operations" title=" graph operations"> graph operations</a> </p> <a href="https://publications.waset.org/abstracts/56602/on-the-zeros-of-the-degree-polynomial-of-a-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">249</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36053</span> From Convexity in Graphs to Polynomial Rings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ladznar%20S.%20Laja">Ladznar S. Laja</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosalio%20G.%20Artes"> Rosalio G. Artes</a>, <a href="https://publications.waset.org/abstracts/search?q=Jr."> Jr.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduced a graph polynomial relating convexity concepts. A graph polynomial is a polynomial representing a graph given some parameters. On the other hand, a subgraph H of a graph G is said to be convex in G if for every pair of vertices in H, every shortest path with these end-vertices lies entirely in H. We define the convex subgraph polynomial of a graph G to be the generating function of the sequence of the numbers of convex subgraphs of G of cardinalities ranging from zero to the order of G. This graph polynomial is monic since G itself is convex. The convex index which counts the number of convex subgraphs of G of all orders is just the evaluation of this polynomial at 1. Relationships relating algebraic properties of convex subgraphs polynomial with graph theoretic concepts are established. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convex%20subgraph" title="convex subgraph">convex subgraph</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20index" title=" convex index"> convex index</a>, <a href="https://publications.waset.org/abstracts/search?q=generating%20function" title=" generating function"> generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20ring" title=" polynomial ring"> polynomial ring</a> </p> <a href="https://publications.waset.org/abstracts/9019/from-convexity-in-graphs-to-polynomial-rings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9019.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36052</span> An Application of Graph Theory to The Electrical Circuit Using Matrix Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samai%27la%20Abdullahi">Samai'la Abdullahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A graph is a pair of two set and so that a graph is a pictorial representation of a system using two basic element nodes and edges. A node is represented by a circle (either hallo shade) and edge is represented by a line segment connecting two nodes together. In this paper, we present a circuit network in the concept of graph theory application and also circuit models of graph are represented in logical connection method were we formulate matrix method of adjacency and incidence of matrix and application of truth table. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=euler%20circuit%20and%20path" title="euler circuit and path">euler circuit and path</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20representation%20of%20circuit%20networks" title=" graph representation of circuit networks"> graph representation of circuit networks</a>, <a href="https://publications.waset.org/abstracts/search?q=representation%20of%20graph%20models" title=" representation of graph models"> representation of graph models</a>, <a href="https://publications.waset.org/abstracts/search?q=representation%20of%20circuit%20network%20using%20logical%20truth%20table" title=" representation of circuit network using logical truth table"> representation of circuit network using logical truth table</a> </p> <a href="https://publications.waset.org/abstracts/32358/an-application-of-graph-theory-to-the-electrical-circuit-using-matrix-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32358.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">561</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36051</span> Building 1-Well-Covered Graphs by Corona, Join, and Rooted Product of Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vadim%20E.%20Levit">Vadim E. Levit</a>, <a href="https://publications.waset.org/abstracts/search?q=Eugen%20Mandrescu"> Eugen Mandrescu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A graph is well-covered if all its maximal independent sets are of the same size. A well-covered graph is 1-well-covered if deletion of every vertex of the graph leaves it well-covered. It is known that a graph without isolated vertices is 1-well-covered if and only if every two disjoint independent sets are included in two disjoint maximum independent sets. Well-covered graphs are related to combinatorial commutative algebra (e.g., every Cohen-Macaulay graph is well-covered, while each Gorenstein graph without isolated vertices is 1-well-covered). Our intent is to construct several infinite families of 1-well-covered graphs using the following known graph operations: corona, join, and rooted product of graphs. Adopting some known techniques used to advantage for well-covered graphs, one can prove that: if the graph G has no isolated vertices, then the corona of G and H is 1-well-covered if and only if H is a complete graph of order two at least; the join of the graphs G and H is 1-well-covered if and only if G and H have the same independence number and both are 1-well-covered; if H satisfies the property that every three pairwise disjoint independent sets are included in three pairwise disjoint maximum independent sets, then the rooted product of G and H is 1-well-covered, for every graph G. These findings show not only how to generate some more families of 1-well-covered graphs, but also that, to this aim, sometimes, one may use graphs that are not necessarily 1-well-covered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maximum%20independent%20set" title="maximum independent set">maximum independent set</a>, <a href="https://publications.waset.org/abstracts/search?q=corona" title=" corona"> corona</a>, <a href="https://publications.waset.org/abstracts/search?q=concatenation" title=" concatenation"> concatenation</a>, <a href="https://publications.waset.org/abstracts/search?q=join" title=" join"> join</a>, <a href="https://publications.waset.org/abstracts/search?q=well-covered%20graph" title=" well-covered graph"> well-covered graph</a> </p> <a href="https://publications.waset.org/abstracts/86859/building-1-well-covered-graphs-by-corona-join-and-rooted-product-of-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">208</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36050</span> Hybrid Approximate Structural-Semantic Frequent Subgraph Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Montaceur%20Zaghdoud">Montaceur Zaghdoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Moussaoui"> Mohamed Moussaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20graph%20matching" title="approximate graph matching">approximate graph matching</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20frequent%20subgraph%20mining" title=" hybrid frequent subgraph mining"> hybrid frequent subgraph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20mining" title=" graph mining"> graph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=possibility%20theory" title=" possibility theory"> possibility theory</a> </p> <a href="https://publications.waset.org/abstracts/34195/hybrid-approximate-structural-semantic-frequent-subgraph-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=elemental%20graph%20data%20model%20%28EGDM%29&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=elemental%20graph%20data%20model%20%28EGDM%29&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=elemental%20graph%20data%20model%20%28EGDM%29&page=4">4</a></li> <li class="page-item"><a class="page-link" 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