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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>
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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 class="masthead">
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<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://icdm22.cse.usf.edu/">ICDM'22</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">More Details!</a>
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<!-- 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 ICDM'22 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>September 17nd, 2022</b> </li>
<li>Notification of Acceptance: <s>September, 23th, 2022</s><b>October, 8th, 2022</b> </li>
<li>Camera-ready paper due: <s>October, 1st, 2022</s> <b>October, 15th, 2022</b> </li>
<li>MLoG at ICDM'22 Workshop day: <b>November 28th, 2022</b> </li>
</ul>
(All deadlines are 11:59pm Pacific Daylight Time. We have updated the above to align with ICDM's key dates.)
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<h2 class="section-heading text-uppercase">Submission Details</h2>
<div style="text-align:left">
All submissions should be 2 to 8 pages (including all references, tables, and figures), double-column pdfs, and following the IEEE conference format - please refer to the ICDM'22 website for futher details <a href="https://icdm22.cse.usf.edu/calls/Papers.html">here</a>.
<br /><br />Submissions will be reviewed 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. The best paper (according to the reviewers' ratings) will be announced at the end of the workshop.
Following ICDM tradition, all accepted works at the MLoG workshop will be published in formal proceedings by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for other workshops (that are archival), conferences, or journals.
<br /><br />To submit your work, please use the following <a href="https://wi-lab.com/cyberchair/2022/icdm22/scripts/submit.php?subarea=S04&undisplay_detail=1&wh=/cyberchair/2022/icdm22/scripts/ws_submit.php">Submission Link</a>
<br /><br />Note that at least one of the authors of the accepted workshop papers must register for the workshop (details to come on the main ICDM'22 website).
For questions about submission, please contact us at: mlog-workshop-2022@googlegroups.com
</div>
</div>
</div>
</div>
</section>
<!-- About -->
<section id="program">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h3 class="section-subheading">Workshop Tentative Program. (Local time EST in Orlando, FL at ICDM'22).</h3>
<div style="text-align:left">
<h3><b>Morning Session (EST)</b></h3>
09:00 - 09:10 Welcome and Opening Remarks<br />
<hr>
09:10 - 10:00 Keynote presentation 1 - Yue Ning<br />
<hr>
10:00 - 10:30 Break <br />
<hr>
10:30 - 11:15 Keynote presentation 2 - Hanghang Tong<br />
11:15 - 12:00 Keynote presentation 3 - Tara Safavi<br />
<hr>
12:00 - 13:00 Lunch break<br />
<hr>
13:00 - 13:15 Contributing paper - Representing Social Networks as Dynamic Heterogeneous Graphs<br />
13:15 - 13:30 Contributing paper - LSP: Acceleration of Graph Neural Networks via Locality Sensitive Pruning of Graphs<br />
<hr>
13:30 - 14:15 Keynote presentation 4 - Bryan Perozzi <br />
14:15 - 15:00 Keynote presentation 5 - Tong Zhao<br />
<hr>
15:00 - 15:30 Break <br />
<hr>
15:30 - 16:15 Keynote presentation 6 - Vagelis Papalexakis<br />
<hr>
16:15 - 16:30 Contributing paper - EnD: Enhanced Dedensification for Graph Compressing and Embedding<br />
<hr>
16:30 - 16:45 Contributing paper - Demystify Degree-related Bias in Link Prediction<br />
<hr>
16:45 - 17:00 Closing Remarks <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">
<p style="display:inline">TBD</p><br />
<a href="https://yue-ning.github.io/"><img src="img/yuening.png" alt=""></a>
<h4><a href="https://yue-ning.github.io/">Yue Ning</a></h4>
<p class="text-muted" style="display:inline">Assistant Professor</p></br>
<p class="text-muted" style="display:inline"> Stevens Institute of Technology</p>
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<p style="display:inline">TBD</p><br />
<a href="https://www.cs.ucr.edu/~epapalex/"><img src="img/vagelispapalexakis.png" alt=""></a>
<h4><a href="https://www.cs.ucr.edu/~epapalex/">Vagelis Papalexakis</a></h4>
<p class="text-muted" style="display:inline">Associate Professor</p></br>
<p class="text-muted" style="display:inline">University of California Riverside</p>
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<p style="display:inline">TBD</p><br />
<a href="http://www.perozzi.net/"><img src="img/bryanperozzi.png" alt=""></a>
<h4><a href="http://www.perozzi.net/">Bryan Perozzi</a></h4>
<p class="text-muted" style="display:inline">Senior Staff Research Scientist</p></br>
<p class="text-muted" style="display:inline">Google Research</p>
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<p style="display:inline">TBD</p><br />
<a href="https://tsafavi.github.io/"><img src="img/tarasafavi.png" alt=""></a>
<h4><a href="https://tsafavi.github.io/">Tara Safavi</a></h4>
<p class="text-muted" style="display:inline">Research Scientist</p></br>
<p class="text-muted" style="display:inline">Microsoft Research</p>
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<p style="display:inline">TBD</p><br />
<a href="http://tonghanghang.org/"><img src="img/hanghangtong.png" alt=""></a>
<h4><a href="http://tonghanghang.org/">Hanghang Tong</a></h4>
<p class="text-muted" style="display:inline">Associate Professor</p></br>
<p class="text-muted" style="display:inline">University of Illinois Urbana-Champaign</p>
</div>
</div>
<div class="col-sm-4">
<div class="keynote">
<p style="display:inline">TBD</p><br />
<a href="https://tzhao.io/"><img src="img/tongzhao.png" alt=""></a>
<h4><a href="https://tzhao.io/">Tong Zhao</a></h4>
<p class="text-muted" style="display:inline">Research Scientist</p></br>
<p class="text-muted" style="display:inline">Snap Research</p>
</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="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="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://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">Senior Machine Learning Engineer</p>
<p class="text-muted">AstraZeneca</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/NeilShah.png" alt=""></a>
<h4><a href="http://nshah.net/">Neil Shah</a></h4>
<p class="text-muted" style="display:inline">Lead Research Scientist</p>
<p class="text-muted">Snap Inc.</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div>
<div class="col-sm-4">
<div class="team-member">
<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>
<p class="text-muted" style="display:inline">ARC Future Fellow and Senior Lecturer</p>
<p class="text-muted">Monash University</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div>
<br /><br />
<br>
<div class="col-lg-12 text-center">
<h4 class="section-heading text-uppercase">Additional Workshop Organizers</h4>
<!-- <div class="row">
<div class="col-sm-4">
<div class="team-member">
<p style="display:inline">Proceedings Chair</p><br />
<a href="tbd"><img style="max-width:none" class="mx-auto" src="img/tbd.png" alt=""></a>
<h4><a href="tbd">TBD</a></h4>
<p class="text-muted" style="display:inline">TBD</p>
<p class="text-muted">TBD</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div> -->
<div class="row">
<div class="col-sm-4">
<div class="team-member">
<p style="display:inline">Web Chair</p><br />
<a href="https://yuwvandy.github.io/"><img style="max-width:none" class="mx-auto" src="img/YuWang.png" alt=""></a>
<h4><a href="https://yuwvandy.github.io/">Yu Wang</a></h4>
<p class="text-muted" style="display:inline">PhD Student</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">
<p style="display:inline">Publicity Chair</p><br />
<a href="https://vijaydwivedi.com.np/"><img style="max-width:none" class="mx-auto" src="img/VijayPrakashDwivedi.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>
<p class="text-muted" >Nanyang Technological University</p>
<ul class="list-inline social-buttons">
</ul>
</div>
</div>
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<a href="https://mlog-workshop.github.io/icdm23">ICDM'23 </a>: December 4th, 2023; Shanghai, China. <br>
<a href="https://mlog-workshop.github.io/wsdm23">WSDM'23 </a>: March 3rd, 2023; Singapore. <br>
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<!-- <a href="https://mlog-workshop.github.io/icdm22">ICDM'22 </a>: November 28th, 2022; Orlando, FL, USA. <br> -->
<a href="https://mlog-workshop.github.io/wsdm22">WSDM'22</a>: February 25th, 2022; Virtual. <br>
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