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<!DOCTYPE html>
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
<meta name="description" content="Tutorial on Hyperbolic Networks: Theory, Architectures and Applications. To be presented at
TheWebConf 2022." />
<meta name="author" content="Nurendra Choudhary" />
<title>Hyperbolic Networks: Theory, Architectures and Applications</title>
<link rel="icon" type="image/x-icon" href="assets/img/favicon.png" />
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<a class="navbar-brand js-scroll-trigger" href="#page-top">
<span class="d-block d-lg-none">Tutorial</span>
</a>
<div class="collapse navbar-collapse" id="navbarResponsive">
<ul class="navbar-nav">
<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#overview">Overview</a></li>
<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#teaser">Teaser Video</a></li>
<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#schedule">Schedule</a></li>
<li class="nav-item"><a class="nav-link js-scroll-trigger" href="#taxonomy">Taxonomy</a></li>
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<span class="d-block d-lg-none">Virginia Tech</span>
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<a href="https://sanghani.cs.vt.edu/" target="_blank" rel="noopener noreferrer">
<img class="img-fluid img-profile mx-auto mb-2" src="assets/img/vt.png" width="70%" alt="Virginia Tech"/>
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<a href="https://www.amazon.science/" target="_blank" rel="noopener noreferrer">
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<span class="d-block d-lg-none">TheWebConf22</span>
<span class="d-none d-lg-block" style="background-color: rgb(300, 300, 300)";>
<a href="https://www2022.thewebconf.org/" target="_blank" rel="noopener noreferrer">
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<section class="resume-section" id="overview">
<div class="resume-section-content">
<h2 class="mb-0">
Hyperbolic Networks:<br>
<span class="text-primary">Theory, Architectures and Applications</span>
</h2>
<h5 class="mb-5" style="font-family: 'Trebuchet MS', 'Lucida Sans Unicode', 'Lucida Grande', 'Lucida Sans', Arial, sans-serif">
Tutorial @ <a href="https://www2022.thewebconf.org/">TheWebconf 2022</a> · Online · Time and Link: TBD ·
</h5>
<!-- <div class="subheading mb-5" style="font-family: 'Trebuchet MS', 'Lucida Sans Unicode', 'Lucida Grande', 'Lucida Sans', Arial, sans-serif">
Tutorial @ <a href="https://www2022.thewebconf.org/">TheWebconf 2022</a> · Online · Time and Link: TBD ·
</div> -->
<p class="lead mb-5">Graphs are ubiquitous data-structures that are widely-used in a
number of data storage scenarios, including social networks, recommender systems,
knowledge graphs and e-commerce. This has led to a rise of GNN architectures to
analyze and encode information from the graphs for better performance in downstream
tasks.
While preliminary research in the domain of graph analysis was driven by neural architectures,
recent studies has revealed important properties unique to graph datasets such as hierarchies and
global structures. This has driven research into hyperbolic space due to their ability to
effectively encode the inherent hierarchy present in graph datasets. The research has also
been subsequently applied to other domains such as NLP and computer vision to get
formidable results. However, the major challenge to further growth
is the obscurity of hyperbolic networks and a better comprehension
of the necessary algebraic operations needed to broaden the application to different
neural network architectures. In this tutorial, we
aim to introduce researchers and practitioners in the web domain
to the hyperbolic equivariants of the Euclidean operations that are
necessary to tackle their application to neural network architectures.
Additionally, we describe the popular hyperbolic variants of
GNN architectures such as recurrent networks, convolution networks and attention networks
and explain their implementation,
in contrast to the Euclidean counterparts. Furthermore, we also
motivate our tutorial through existing applications in the areas of
graph analysis, knowledge graph reasoning, product search, NLP,
and computer vision and compare the performance gains to the
Euclidean counterparts.</p>
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<h2 class="mb-5">Teaser Video</h2>
<style>
.embed-container
{ position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; }
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</div>
</section>
<hr class="m-0" />
<section class="resume-section" id="schedule">
<div class="resume-section-content">
<h2 class="mb-5">Schedule</h2>
<div class="row">
<div class="col-4">
<div class="list-group" id="list-tab" role="tablist">
<a class="list-group-item list-group-item-action active" id="list-introduction-list" data-toggle="list" href="#list-introduction" role="tab" aria-controls="introduction">Introduction <i class="fas fa-user-clock"></i> 15 min</a>
<a class="list-group-item list-group-item-action" id="list-theory-list" data-toggle="list" href="#list-theory" role="tab" aria-controls="theory">Theory <i class="fas fa-user-clock"></i> 50 min</a>
<a class="list-group-item list-group-item-action" id="list-architectures-list" data-toggle="list" href="#list-architectures" role="tab" aria-controls="architectures">Architectures <i class="fas fa-user-clock"></i> 50 min</a>
<a class="list-group-item list-group-item-action" id="list-applications-list" data-toggle="list" href="#list-applications" role="tab" aria-controls="applications">Applications <i class="fas fa-user-clock"></i> 50 min</a>
<a class="list-group-item list-group-item-action" id="list-conclusion-list" data-toggle="list" href="#list-conclusion" role="tab" aria-controls="conclusion">Conclusion and Future Work <i class="fas fa-user-clock"></i> 15 min</a>
</div>
</div>
<div class="col-8">
<div class="tab-content" id="nav-tabContent">
<div class="tab-pane fade show active" id="list-introduction" role="tabpanel" aria-labelledby="list-introduction-list">
<ul class="list-group">
<li class="list-group-item">Motivation <i class="fas fa-clock"></i> 5 min </li>
<li class="list-group-item">Relevance of Hyperbolic Neural Networks <i class="fas fa-clock"></i> 10 min</li>
</ul>
</div>
<div class="tab-pane fade" id="list-theory" role="tabpanel" aria-labelledby="list-theory-list">
<ul class="list-group">
<li class="list-group-item">Introduction to Euclidean operations <i class="fas fa-clock"></i> 10 min </li>
<li class="list-group-item">Introduction to Hyperbolic space <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Hyperbolic operations <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Hyperbolic MLR and activation <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Riemannian optimization <i class="fas fa-clock"></i> 10 min</li>
</ul>
</div>
<div class="tab-pane fade" id="list-architectures" role="tabpanel" aria-labelledby="list-architectures-list">
<ul class="list-group">
<li class="list-group-item">Hyperbolic Linear Layer <i class="fas fa-clock"></i> 10 min </li>
<li class="list-group-item">Hyperbolic Recurrent Layer <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Hyperbolic Convolution Layer <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Hyperbolic Transformers <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Hyperbolic Neural Networks ++ <i class="fas fa-clock"></i> 10 min</li>
</ul>
</div>
<div class="tab-pane fade" id="list-applications" role="tabpanel" aria-labelledby="list-applications-list">
<ul class="list-group">
<li class="list-group-item">Graph Applications <i class="fas fa-clock"></i> 10 min </li>
<li class="list-group-item">Knowledge Graph Reasoning <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Product Search <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Natural Language Processing <i class="fas fa-clock"></i> 10 min</li>
<li class="list-group-item">Computer Vision <i class="fas fa-clock"></i> 10 min</li>
</ul>
</div>
<div class="tab-pane fade" id="list-conclusion" role="tabpanel" aria-labelledby="list-conclusion-list">
<li class="list-group-item">Summary <i class="fas fa-clock"></i> 5 min </li>
<li class="list-group-item">Broader Impact <i class="fas fa-clock"></i> 5 min </li>
<li class="list-group-item">Future Directions <i class="fas fa-clock"></i> 5 min </li>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<hr class="m-0" />
<section class="resume-section" id="taxonomy">
<div class="resume-section-content">
<h2 class="mb-5">Taxonomy</h2>
<img src="assets/img/taxonomy.png" class="img-fluid" alt="Responsive image" width="75%">
</div>
</section>
<hr class="m-0" />
<hr class="m-0" />
<!-- Awards-->
<section class="resume-section" id="resources">
<div class="resume-section-content">
<h2 class="mb-5">Resources</h2>
<ul class="fa-ul mb-0">
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fab fa-github"></i></span>
Code coming soon!
</li>
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fas fa-book-open"></i></span>
Choudhary, N., Rao, N., Katariya, S., Subbian, K., & Reddy, C. K. (2022, February). ANTHEM: Attentive Hyperbolic Entity Model for Product Search. In Proceedings of the International Conference on Web Search and Data Mining 2022.
(<a href="https://people.cs.vt.edu/~reddy/papers/WSDM22.pdf" target="_blank" rel="noopener noreferrer">pdf</a>)
</li>
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fas fa-book-open"></i></span>
Choudhary, N., Rao, N., Katariya, S., Subbian, K., & Reddy, C. (2021). Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs. Advances in Neural Information Processing Systems, 34.
(<a href="https://people.cs.vt.edu/~reddy/papers/NeurIPS21.pdf" target="_blank" rel="noopener noreferrer">pdf</a>)
</li>
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fas fa-book-open"></i></span>
Choudhary, N., Rao, N., Katariya, S., Subbian, K., & Reddy, C. K. (2021, April). Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs. In Proceedings of the Web Conference 2021 (pp. 1373-1384).
(<a href="https://people.cs.vt.edu/~reddy/papers/WWW21a.pdf" target="_blank" rel="noopener noreferrer">pdf</a>)
</li>
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fas fa-book-open"></i></span>
Chami, I., Ying, Z., Ré, C., & Leskovec, J. (2019). Hyperbolic graph convolutional neural networks. Advances in neural information processing systems, 32, 4868-4879.
(<a href="https://arxiv.org/pdf/1910.12933.pdf" target="_blank" rel="noopener noreferrer">pdf</a>)
</li>
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fas fa-book-open"></i></span>
Ganea, O. E., Bécigneul, G., & Hofmann, T. (2018). Hyperbolic neural networks. Advances in neural information processing systems, 5345-5355.
(<a href="https://arxiv.org/pdf/1805.09112.pdf" target="_blank" rel="noopener noreferrer">pdf</a>)
</li>
<li>
<span class="fa-li" style="color: #BD5D38;"><i class="fas fa-book-open"></i></span>
Shimizu, R., Mukuta, Y., & Harada, T. (2021). Hyperbolic neural networks++. International Conference on Learning Representations.
(<a href="https://arxiv.org/pdf/2006.08210.pdf" target="_blank" rel="noopener noreferrer">pdf</a>)
</li>
</ul>
</div>
</section>
<hr class="m-0" />
<section class="resume-section" id="speakers">
<div class="resume-section-content">
<h2 class="mb-5">Speakers</h2>
<div class="d-flex flex-column flex-md-row justify-content-between">
<a class="navbar-brand js-scroll-trigger" href="https://nurendra.me/">
<span class="d-block d-lg-none">Nurendra Choudhary</span>
<span class="d-none d-lg-block"><img class="img-fluid img-profile rounded-circle mx-auto mb-2" src="assets/img/nurendra.jpeg" alt="Chandan Reddy" /></span>
</a>
<div class="flex-grow-1">
<h3 class="mb-0">Nurendra Choudhary</h3>
<div class="subheading mb-3">Ph.D. student, Virginia Tech</div>
<p>Nurendra Choudhary is a Ph.D. student in the department of
Computer Science at Virginia Tech. His research, under advisor Dr.
Chandan Reddy, is focused on representation learning in the fields
of graph analysis and product search. He has published several peer-
reviewed papers in top-tier conferences including WWW, NeurIPS,
WSDM, and COLING. He has received his M.S. in Computational
Linguistics from International Institute of Information Technology,
during which he received the Best Paper Award at CICLING, 2018.</p>
</div>
<!-- <div class="flex-shrink-0"><span class="text-primary">September 2008 - June 2010</span></div> -->
</div>
<br><br>
<div class="d-flex flex-column flex-md-row justify-content-between mb-5">
<a class="navbar-brand js-scroll-trigger" href="https://sites.google.com/view/raonikhil/home">
<span class="d-block d-lg-none">Nikhil Rao</span>
<span class="d-none d-lg-block"><img class="img-fluid img-profile rounded-circle mx-auto mb-2" src="assets/img/nikhil.jpeg" alt="Chandan Reddy" /></span>
</a>
<div class="flex-grow-1">
<h3 class="mb-0">Nikhil Rao</h3>
<div class="subheading mb-3">Senior Scientist, Amazon</div>
<p>Nikhil Rao is a senior scientist at Amazon where he works on large
scale graph modeling and algorithms to improve Amazon Search.
Prior to joining Amazon, he was a researcher at Technicolor AI
Labs in Palo Alto. Nikhil's research interests and expertise span
large scale optimization, data modeling and mining, and developing
algorithms that take advantage of structure present in the data.
Nikhil has published several papers in top-tier conferences and
journals. He is the recipient of the ICES Post Doctoral Fellowship
award from UT Austin, and the IEEE Best Student Paper award.
He holds a PhD in Electrical and Computer Engineering from UW
Madison.</p>
</div>
<!-- <div class="flex-shrink-0"><span class="text-primary">December 2011 - March 2013</span></div> -->
</div>
<div class="d-flex flex-column flex-md-row justify-content-between mb-5">
<a class="navbar-brand js-scroll-trigger" href="https://sites.google.com/site/mailtosuka/">
<span class="d-block d-lg-none">Karthik Subbian</span>
<span class="d-none d-lg-block"><img class="img-fluid img-profile rounded-circle mx-auto mb-2" src="assets/img/karthik.jpeg" alt="Chandan Reddy" /></span>
</a>
<div class="flex-grow-1">
<h3 class="mb-0">Karthik Subbian</h3>
<div class="subheading mb-3">Principal Scientist, Amazon</div>
<p>Karthik Subbian is a principal scientist at Amazon with more than
17 years of industry experience. He leads a team of scientists and
engineers to improve search quality and trust. He was a research
scientist and lead at Facebook, before coming to Amazon, where he
had led a team of scientists and engineers to explore information
propagation and user modeling problems using the social network
structure and its interactions. Earlier to that, he was working at IBM
T.J. Watson research center in the Business Analytics and Mathe-
matical Sciences division. His areas of expertise include machine
learning, information retrieval, and large-scale network analysis.
More specifically, semi-supervised and supervised learning in networks,
personalization and recommendation, information diffusion,
and representation learning. He holds a masters degree from the
Indian Institute of Science (IISc) and a Ph.D. from the University of
Minnesota, both in computer science. Karthik has won numerous
prestigious awards, including the IBM Ph.D. fellowship, best paper
award at SIAM Data Mining (SDM) conference 2013, and INFORMS
Edelman laureate award 2013.</p>
</div>
<!-- <div class="flex-shrink-0"><span class="text-primary">July 2010 - December 2011</span></div> -->
</div>
<div class="d-flex flex-column flex-md-row justify-content-between">
<a class="navbar-brand js-scroll-trigger" href="https://www.amazon.science/author/srinivasan-h-sengamedu">
<span class="d-block d-lg-none">Srinivasan H. Sengamedu</span>
<span class="d-none d-lg-block"><img class="img-fluid img-profile rounded-circle mx-auto mb-2" src="assets/img/shs.png" alt="Chandan Reddy" /></span>
</a>
<div class="flex-grow-1">
<h3 class="mb-0">Srinivasan H. Sengamedu</h3>
<div class="subheading mb-3">Senior Machine Learning Managaer, Amazon</div>
<p>Srinivasan H. Sengamedu is a Senior Machine Learning Manager
at Amazon where he currently works on analysis of software using
machine learning. The techniques are used in Amazon CodeGuru
Reviewer. He has earlier worked on several applications of machine
learning such as fake reviews, ranking problems, online advertising,
information extraction, and comment spam. He has published on
these topics in top-tier conferences. He holds a PhD from Indian
Institute of Science.</p>
</div>
<!-- <div class="flex-shrink-0"><span class="text-primary">September 2008 - June 2010</span></div> -->
</div>
<br><br>
<div class="d-flex flex-column flex-md-row justify-content-between mb-5">
<a class="navbar-brand js-scroll-trigger" href="https://people.cs.vt.edu/~reddy/">
<span class="d-block d-lg-none">Chandan Reddy</span>
<span class="d-none d-lg-block"><img class="img-fluid img-profile rounded-circle mx-auto mb-2" src="assets/img/reddy.jpeg" alt="Chandan Reddy" /></span>
</a>
<div class="flex-grow-1">
<h3 class="mb-0">Chandan Reddy</h3>
<div class="subheading mb-3">Professor, Department of Computer Science, Virginia Tech</div>
<p>Chandan Reddy received his Ph.D. from Cornell University and M.S. from Michigan State University.
His primary research
interests are Data Mining and Machine Learning with applications
to Healthcare Analytics and Social Network Analysis. His research
has been funded by NSF, NIH, DOE, DOT, and various industries.
He has published over 140 peer-reviewed articles in leading conferences
and journals. He received several awards for his research
work including the Best Application Paper Award at ACM SIGKDD
conference in 2010, Best Poster Award at IEEE VAST conference in
2014, Best Student Paper Award at IEEE ICDM conference in 2016,
and was a finalist of the INFORMS Franz Edelman Award Competition
in 2011. He is serving on the editorial boards of ACM TKDD,
ACM TIST, and IEEE Big Data journals. He is a senior member of
the IEEE and distinguished member of the ACM.</p>
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