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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.09657">arXiv:2407.09657</a> <span> [<a href="https://arxiv.org/pdf/2407.09657">pdf</a>, <a href="https://arxiv.org/format/2407.09657">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Analyzing X's Web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Baekey%2C+A">Alexander Baekey</a>, <a href="/search/cs?searchtype=author&query=Jayalath%2C+C">Chathura Jayalath</a>, <a href="/search/cs?searchtype=author&query=Mantzaris%2C+A">Alexander Mantzaris</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.09657v1-abstract-short" style="display: inline;"> The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.09657v1-abstract-full').style.display = 'inline'; document.getElementById('2407.09657v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.09657v1-abstract-full" style="display: none;"> The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights into information flow but prove insufficient for identifying hidden influencers in large-scale social media networks such as X (previously known as Twitter). This study adopts and enhances a non-parametric framework based on Transfer Entropy to elucidate the influence relationships among X users. It further categorizes the distribution of influence exerted by these actors through the innovative use of multiplex network measures within a social media context, aiming to pinpoint influential actors during significant world events. The methodology was applied to three distinct events, and the findings revealed that actors in different events leveraged different types of news articles and influenced distinct sets of actors based on the news category. Notably, we found that actors disseminating trustworthy news articles to influence others occasionally resort to untrustworthy sources. However, the converse scenario, wherein actors predominantly using untrustworthy news types switch to trustworthy sources for influence, is less prevalent. This asymmetry suggests a discernible pattern in the strategic use of news articles for influence across social media networks, highlighting the nuanced roles of trustworthiness and popularity in the spread of information and influence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.09657v1-abstract-full').style.display = 'none'; document.getElementById('2407.09657v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at the Advances in Social Networks Analysis and Mining (ASONAM) - 2024, Annual Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11656">arXiv:2401.11656</a> <span> [<a href="https://arxiv.org/pdf/2401.11656">pdf</a>, <a href="https://arxiv.org/format/2401.11656">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations' Containment Impact </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Yalabadi%2C+A+K">Ali Khodabandeh Yalabadi</a>, <a href="/search/cs?searchtype=author&query=Yazdani-Jahromi%2C+M">Mehdi Yazdani-Jahromi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11656v1-abstract-short" style="display: inline;"> Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the lim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11656v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11656v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11656v1-abstract-full" style="display: none;"> Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives. Our results indicate that, among various factors, the frequency of cleaning intervals is the most critical element for controlling the spread of CDI in a hospital environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11656v1-abstract-full').style.display = 'none'; document.getElementById('2401.11656v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted and presented at the Computational Social Science Society of the Americas Conference (CSS 2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11524">arXiv:2401.11524</a> <span> [<a href="https://arxiv.org/pdf/2401.11524">pdf</a>, <a href="https://arxiv.org/format/2401.11524">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yalabadi%2C+A+K">Ali Khodabandeh Yalabadi</a>, <a href="/search/cs?searchtype=author&query=Yazdani-Jahromi%2C+M">Mehdi Yazdani-Jahromi</a>, <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+I">Ivan Garibay</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11524v1-abstract-short" style="display: inline;"> The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-bas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11524v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11524v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11524v1-abstract-full" style="display: none;"> The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11524v1-abstract-full').style.display = 'none'; document.getElementById('2401.11524v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at The Computational Social Science Society of the Americas (CSS) - 2023, Annual Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.02326">arXiv:2311.02326</a> <span> [<a href="https://arxiv.org/pdf/2311.02326">pdf</a>, <a href="https://arxiv.org/format/2311.02326">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yalabadi%2C+A+K">Ali Khodabandeh Yalabadi</a>, <a href="/search/cs?searchtype=author&query=Yazdani-Jahromi%2C+M">Mehdi Yazdani-Jahromi</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Tayebi%2C+A">Aida Tayebi</a>, <a href="/search/cs?searchtype=author&query=Abdidizaji%2C+S">Sina Abdidizaji</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.02326v1-abstract-short" style="display: inline;"> Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our informa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.02326v1-abstract-full').style.display = 'inline'; document.getElementById('2311.02326v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.02326v1-abstract-full" style="display: none;"> Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our information-rich representations for both proteins and drugs offer a detailed perspective on their interaction. Inspired by the Perceiver IO framework, our model features a learnable latent array, initially interacting with protein binding site embeddings using cross-attention and later refined through self-attention and used as a query to the drug fragments in the drug's cross-attention transformer block. This learnable query array serves as a mediator and enables seamless information translation, preserving critical nuances in drug-protein interactions. Our computational results on three benchmarking datasets demonstrate the superior predictive power of our model over several state-of-the-art models. We also show the interpretability of our model in terms of the critical components of both target proteins and drug molecules within drug-target pairs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.02326v1-abstract-full').style.display = 'none'; document.getElementById('2311.02326v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at the NeurIPS workshop (AI4D3) - 2023</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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