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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="">
<meta name="author" content="">
<title>Machine Learning on Graphs (MLoG) Workshop</title>
<!-- Bootstrap core CSS -->
<link href="vendor/bootstrap/css/bootstrap.min.css" rel="stylesheet">
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<a class="nav-link js-scroll-trigger" href="#home">Home</a>
</li>
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<a class="nav-link js-scroll-trigger" href="#toi">Topics of Interest</a>
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<a class="nav-link js-scroll-trigger" href="#dates">Important Dates</a>
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<!-- Header -->
<header class="masthead">
<div class="container">
<div class="intro-text">
<div class="intro-heading text-uppercase">Machine Learning on Graphs</div>
<div class="intro-lead-in">MLoG Workshop at <a href="https://www.wsdm-conference.org/2023/">WSDM'23</a></div>
<!-- <div class="intro-lead-in">Proceedings are available <a href="http://ceur-ws.org/Vol-2635/">on-line</a>.</div> -->
<a class="btn btn-primary btn-xl text-uppercase js-scroll-trigger" href="#home">Tell Me More!</a>
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</header>
<!-- Services -->
<section id="home">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">About</h2>
<!--<h3 class="section-subheading text-muted">Lorem ipsum dolor sit amet consectetur.</h3>-->
<p style="text-align:justify">
Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way.
</p>
<p style="text-align:justify">
MLoG at WSDM'23 provides a venue to gather the academia researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.
</p>
<br />
</div>
</div>
</div>
</section>
<section class="bg-light" id="toi">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">Topics of Interest</h2>
<div style="text-align:left">
Graphs are a kind of universal data structure for representing pair-wise relationships between entities, which can be ubiquitously observed in different domains ranging from computer science, social science, physics, chemistry, to biology. Many real-world applications can be treated as computational tasks on graphs. For example, friend recommendation in social networks can be regarded as a link prediction task and predicting properties of chemical compounds can be treated as a graph classification task. To facilitate these tasks, machine learning techniques have been widely adopted to perform analysis. As our ability of generating and collecting data constantly increasing in a unprecedented way, the graph-structure data we are facing in the modern era (especially coming from the web) are becoming more and more diverse, complex and large-scale. Hence, more efforts are required for developing more effective algorithms and deploying them efficiently for real-world applications. In this workshop, we aim to discuss the recent research progress of machine learning on graphs in both theoretical foundations and practical applications. We invite submissions that focus on recent advances in research/development of machine learning on graphs along with their applications.
<br /> <br />
Theory and methodology papers are welcome from any of the following areas, including but not limited to:
<br />
<ul>
<li>Graph Kernels</li>
<li>Graph Summarization</li>
<li>Graph Coarsening</li>
<li>Graph Alignment</li>
<li>Graph Generative Models</li>
<li>Graph Mining</li>
<li>Graph Neural Networks</li>
<li>Network Embedding</li>
<li>Machine Learning for Graph Combinatorial Optimization</li>
<li>Graph Feature Engineering and Selection</li>
<li>Scalable Graph Learning Models and Methods</li>
</ul>
and application papers focused on but not limited to:<br />
<ul>
<li>Recommender Systems</li>
<li>Computer Vision</li>
<li>Natural Language Processing</li>
<li>Bioinformatics (e.g., drug discovery)</li>
<li>Cybersecurity (e.g., malware detection/propagation)</li>
<li>Financial security (e.g., fraudster detection)</li>
<li>Transportation/mobility networks (e.g., traffic prediction)</li>
<li>Graph ML Platforms and Systems (e.g., in-database machine learning)</li>
</ul>
<br /><br />
</div>
</div>
</div>
</div>
</section>
<section id="dates">
<div class="container">
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<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">Important Dates</h2>
<div style="text-align:left">
<ul>
<li>Submission deadline: <b>January 15th, 2023</b> </li>
<li>Notification of Acceptance: <b>February, 1st, 2023</b> </li>
<li>Camera-ready paper due: <b>February, 17th, 2023</b> </li>
<li>MLoG at WSDM'23 Workshop day: <b>March 3rd, 2023</b> </li>
</ul>
<!-- <div style="text-align:center"><a class="btn btn-primary btn-xl text-uppercase js-scroll-trigger"
href="">Read
CFP</a> -->
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<h2 class="section-heading text-uppercase">Submission Details</h2>
<div style="text-align:left">
We invite both long research papers (5-10 pages) and short research/application papers (2-4 pages) including references. All submissions must be in PDF format and formatted according to the new ACM format published in <a href='https://www.acm.org/publications/proceedings-template'>ACM guidelines</a> (e.g., using the ACM LaTeX template on Overleaf <a href="https://www.overleaf.com/gallery/tagged/acm-official#.WOuOk2e1taQ">here</a>) and selecting the "sigconf" sample. Following the WSDM conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted works will be assessed based on their novelty, technical quality, potential impact, and clarity of writing (and should be in English). For papers that primarily rely on empirical evaluations, the experimental settings and results should be clearly presented and repeatable. We encourage authors to make data and code available publicly when possible. Accepted papers will be posted on this workshop website and will not appear in the WSDM proceedings and are thus <b>non-archival</b> (allowing you to submit works to MLoG at WSDM'23 even if they are current under review elsewhere). The best paper (according to the reviewers' ratings and organizing committee) will be announced at the end of the workshop.<br /><br />
All submissions must be uploaded electronically to EasyChair at: <a href="https://easychair.org/my/conference?conf=wsdm2023mlog#">Submission Page</a><br /><br />
At least one of the authors of the accepted workshop papers must register for the workshop and be present on the day of the workshop.<br /><br />
For questions regarding submissions, please contact us at: wsdm2023mlog@easychair.org
</div>
</div>
</div>
</div>
</section>
<!-- About -->
<section id="program">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">Workshop Program</h2>
<h3 class="section-subheading text-muted">Accepted Papers</h3>
<ul style="text-align:justify">
<li><a href="papers/wsdm23/2023-WSDM-MLoG-Creating_Generalizable_Downstream_Graph_Models_with_Random_Projections.pdf">Creating Generalizable Downstream Graph Models with Random Projections</a>. <br>Anton Amirov, Chris Quirk, and Jennifer Neville</li><br>
<li><a href="papers/wsdm23/2023-WSDM-MLoG-GraphStructured_Crawling_Model_and_Approach.pdf">Graph-Structured Crawling: Model and Approach</a>. <br>Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, and Sasan Tavakkol (authors listed in alphabetical order)</li><br>
<li><a href="papers/wsdm23/2023-WSDM-MLoG-GLobal_Counterfactual_Explainer_for_Graph_Neural_Networks.pdf">Global Counterfactual Explainer for Graph Neural Networks</a>. <br>Zexi Huang*, Mert Kosan*, Sourav Medya, Sayan Ranu, and Ambuj Singh (* co-first author)<br> ***Best Paper Award*** </li><br>
<li><a href="papers/wsdm23/2023-WSDM-MLoG-MetapathBased_DataAugmentation_for_Knowledge_graphs.pdf">Metapath-Based Data-Augmentation for Knowledge Graphs</a>. <br>Saurav Manchanda</li><br>
<li><span style="color:#fed136">Goodness-of-Fit of Attributed Probabilistic Graph Generative Models</span>. <br>Pablo Robles-Granda, Katherine Tsai, and Oluwasanmi Koyejo</li><br>
<li><a href="papers/wsdm23/2023-WSDM-MLoG-DEMOMOTIF_Demographic_Inference_from_Sparse_Records_of_Shopping_Transactions_based_on_Motif_Patterns.pdf">DEMOMOTIF: Demographic Inference from Sparse Records of Shopping Transactions based on Motif Patterns</a>. <br>Jiayun Zhang, Xinyang Zhang, Dezhi Hong, Rajesh Gupta, and Jingbo Shang</li><br>
<li><a href="papers/wsdm23/2023-WSDM-MLoG-Smooth_Anonymity_for_Sparse_Graphs.pdf">Smooth Anonymity for Sparse Graphs</a>. <br>Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni, Andres Munoz Medina and Sergei Vassilvitskii</li><br>
</ul>
</ul>
<h3 class="section-subheading">Workshop Program. (Local time GMT+8 in Singapore at WSDM'23).</h3>
<div style="text-align:left">
08:30 - 09:15 Keynote - Wenqi Fan of PolyU (virtual)<br />
09:15 - 10:00 Keynote - Yuan Fang of SMU (in-person)<br />
<hr>
10:00 - 10:30 Coffee break<br />
<hr>
10:30 - 11:15 Keynote - Feng Xia of RMIT (virtual)<br />
11:15 - 11:30 Contributing Talk - Mert Kosan of UCSB (in-person)<br />
11:30 - 11:45 Contributing Talk - Anton Amirov of Microsoft (in-person)<br />
11:45 - 12:00 Contributing Talk - Hossein Esfandiari of Google (in-person)<br />
<hr>
12:00 - 13:30 Lunch Break<br />
<hr>
13:30 - 14:15 Keynote - Vijay Prakash Dwivedi of NTU (virtual)<br />
14:15 - 15:00 Keynote - Bryan Hooi of NUS (in-person)<br />
<hr>
15:00 - 15:30 Coffee break<br />
<hr>
15:30 - 16:15 Keynote - Shuo Yu of DUT (virtual)<br />
16:15 - 17:00 Keynote - Xiao Huang of PolyU (in-person)<br />
17:00 Final Remarks & Best Paper Award Announcement<br />
</div>
</div>
</div>
</section>
<section class='bg-light' id="keynote">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">Keynote Speakers</h2>
<div class="row">
<div class="col-sm-4">
<div class="keynote">
<a href="https://vijaydwivedi.com.np/"><img src="img/vijay.png" alt=""></a>
<h4><a href="https://vijaydwivedi.com.np/">Vijay Prakash Dwivedi</a></h4>
<p class="text-muted" style="display:inline">PhD Student</p></br>
<p class="text-muted" style="display:inline"> Nanyang Technological University</p></br>
<p style="display:inline">Transformers for Graph Structured Data</p><br />
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<a href="https://wenqifan03.github.io/"><img src="img/wenqi.png" alt=""></a>
<h4><a href="https://wenqifan03.github.io/">Wenqi Fan</a></h4>
<p class="text-muted" style="display:inline">Research Assistant Professor</p></br>
<p class="text-muted" style="display:inline">Hong Kong Polytechnic University</p></br>
<p style="display:inline">Towards Trustworthy Recommender Systems: Models, Vulnerabilities and Robustness</p><br />
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<a href="https://www.yfang.site/"><img src="img/yuan.png" alt=""></a>
<h4><a href="https://www.yfang.site/">Yuan Fang</a></h4>
<p class="text-muted" style="display:inline">Assistant Professor</p></br>
<p class="text-muted" style="display:inline">Singapore Management University</p></br>
<p style="display:inline">Low-resource Learning on Graphs</p><br />
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<a href="https://bhooi.github.io/"><img src="img/bryan.png" alt=""></a>
<h4><a href="https://bhooi.github.io/">Bryan Hooi</a></h4>
<p class="text-muted" style="display:inline">Assistant Professor</p></br>
<p class="text-muted" style="display:inline">National University of Singapore</p></br>
<p style="display:inline">Graph Learning Meets Language Models</p><br />
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<a href="https://www4.comp.polyu.edu.hk/~xiaohuang/"><img src="img/xiao.png" alt=""></a>
<h4><a href="https://www4.comp.polyu.edu.hk/~xiaohuang/">Xiao Huang</a></h4>
<p class="text-muted" style="display:inline">Assistant Professor</p></br>
<p class="text-muted" style="display:inline">Hong Kong Polytechnic University</p></br>
<p style="display:inline">Cross-Correlated Graph Neural Networks - Theory and Applications</p><br />
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<a href="https://www.xia.ai/"><img src="img/feng.png" alt=""></a>
<h4><a href="https://www.xia.ai/">Feng Xia</a></h4>
<p class="text-muted" style="display:inline">Professor</p></br>
<p class="text-muted" style="display:inline">Royal Melbourne Institute of Technology</p></br>
<p style="display:inline">Challenges and Advances in Trustworthy Graph Learning</p><br />
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<a href="http://www.shuo-yu.com/"><img src="img/shuo.png" alt=""></a>
<h4><a href="http://www.shuo-yu.com/">Shuo Yu</a></h4>
<p class="text-muted" style="display:inline">Associate Professor</p></br>
<p class="text-muted" style="display:inline">Dalian University of Technology</p></br>
<p style="display:inline">Deep Graph Learning: Data, Methods, and Applications</p><br />
</div>
</div>
</div>
</div>
</section>
<!-- Team -->
<section id="team">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h2 class="section-heading text-uppercase">Organization</h2>
<!--<h3 class="section-subheading text-muted">Lorem ipsum dolor sit amet consectetur.</h3>-->
</div>
</div>
<br>
<div class="col-lg-12 text-center">
<h3 class="section-heading text-uppercase">Workshop Co-Chairs</h3>
</div>
<div class="row">
<div class="col-sm-4">
<div class="team-member">
<a href="http://www.TylerDerr.com"><img style="max-width:none" class="mx-auto" src="img/TylerDerr.png" alt=""></a>
<h4><a href="http://www.TylerDerr.com">Tyler Derr</a></h4>
<p class="text-muted" style="display:inline">Assistant Professor</p>
<p class="text-muted">Vanderbilt University</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div>
<div class="col-sm-4">
<div class="team-member">
<a href="https://web.njit.edu/~ym329/"><img style="max-width:none" class="mx-auto" src="img/YaoMa2.png" alt=""></a> <!-- rounded-circle-->
<h4><a href="https://web.njit.edu/~ym329/">Yao Ma</a></h4>
<p class="text-muted" style="display:inline">Assistant Professor</p>
<p class="text-muted">New Jersey Institute of Technology</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div>
<div class="col-sm-4">
<div class="team-member">
<a href="https://scholar.google.com/citations?user=pa8DvbkAAAAJ&hl=en"><img style="max-width:none" class="mx-auto" src="img/benedek.png" alt=""></a>
<h4><a href="https://scholar.google.com/citations?user=pa8DvbkAAAAJ&hl=en">Benedek Rozemberczki</a></h4>
<p class="text-muted" style="display:inline">Research Scientist</p>
<p class="text-muted">Isomorphic Labs</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div>
<div class="col-sm-4">
<div class="team-member">
<a href="http://nshah.net/"><img style="max-width:none" class="mx-auto" src="img/neil.png" alt=""></a>
<h4><a href="http://nshah.net/">Neil Shah</a></h4>
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<p class="text-muted">Snap Inc.</p>
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<a href="https://shiruipan.github.io/"><img style="max-width:none" class="mx-auto" src="img/ShiruiPan.png" alt=""></a>
<h4><a href="https://shiruipan.github.io/">Shirui Pan</a></h4>
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<a href="https://yunchaoliu.github.io/"><img style="max-width:none" class="mx-auto" src="img/lance.png" alt=""></a>
<h4><a href="https://yunchaoliu.github.io/">Yunchao "Lance" Liu</a></h4>
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<p class="text-muted" >Vanderbilt University</p>
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<h4><a href="https://yuyingzhao.github.io/">Yuying Zhao</a></h4>
<p class="text-muted" style="display:inline">PhD Student</p>
<p class="text-muted" >Vanderbilt University</p>
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