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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/2411.11848">arXiv:2411.11848</a> <span> [<a href="https://arxiv.org/pdf/2411.11848">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistical Finance">q-fin.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Robust Graph Neural Networks for Stability Analysis in Dynamic Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-fin?searchtype=author&query=Zhang%2C+X">Xin Zhang</a>, <a href="/search/q-fin?searchtype=author&query=Xu%2C+Z">Zhen Xu</a>, <a href="/search/q-fin?searchtype=author&query=Liu%2C+Y">Yue Liu</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+M">Mengfang Sun</a>, <a href="/search/q-fin?searchtype=author&query=Zhou%2C+T">Tong Zhou</a>, <a href="/search/q-fin?searchtype=author&query=Sun%2C+W">Wenying Sun</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="2411.11848v1-abstract-short" style="display: inline;"> In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dyna… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11848v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11848v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11848v1-abstract-full" style="display: none;"> In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11848v1-abstract-full').style.display = 'none'; document.getElementById('2411.11848v1-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> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">It was accepted by the 3rd International Conference on Cloud Computing Big Data Application and Software Engineering</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1210.4900">arXiv:1210.4900</a> <span> [<a href="https://arxiv.org/pdf/1210.4900">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Trading and Market Microstructure">q-fin.TR</span> </div> </div> <p class="title is-5 mathjax"> Probability and Asset Updating using Bayesian Networks for Combinatorial Prediction Markets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-fin?searchtype=author&query=Sun%2C+W">Wei Sun</a>, <a href="/search/q-fin?searchtype=author&query=Hanson%2C+R">Robin Hanson</a>, <a href="/search/q-fin?searchtype=author&query=Laskey%2C+K+B">Kathryn Blackmond Laskey</a>, <a href="/search/q-fin?searchtype=author&query=Twardy%2C+C">Charles Twardy</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="1210.4900v1-abstract-short" style="display: inline;"> A market-maker-based prediction market lets forecasters aggregate information by editing a consensus probability distribution either directly or by trading securities that pay off contingent on an event of interest. Combinatorial prediction markets allow trading on any event that can be specified as a combination of a base set of events. However, explicitly representing the full joint distribution… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1210.4900v1-abstract-full').style.display = 'inline'; document.getElementById('1210.4900v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1210.4900v1-abstract-full" style="display: none;"> A market-maker-based prediction market lets forecasters aggregate information by editing a consensus probability distribution either directly or by trading securities that pay off contingent on an event of interest. Combinatorial prediction markets allow trading on any event that can be specified as a combination of a base set of events. However, explicitly representing the full joint distribution is infeasible for markets with more than a few base events. A factored representation such as a Bayesian network (BN) can achieve tractable computation for problems with many related variables. Standard BN inference algorithms, such as the junction tree algorithm, can be used to update a representation of the entire joint distribution given a change to any local conditional probability. However, in order to let traders reuse assets from prior trades while never allowing assets to become negative, a BN based prediction market also needs to update a representation of each user's assets and find the conditional state in which a user has minimum assets. Users also find it useful to see their expected assets given an edit outcome. We show how to generalize the junction tree algorithm to perform all these computations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1210.4900v1-abstract-full').style.display = 'none'; document.getElementById('1210.4900v1-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> 16 October, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2012. </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">Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> UAI-P-2012-PG-815-824 </p> </li> </ol> <div class="is-hidden-tablet">  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