Hyperbolic Networks:
Theory, Architectures and Applications

Tutorial @ AAAI 2023 · Washington D.C., USA · Time TBD.
Code Library: GraphZoo: Facilitating learning, using, and designing graph processing pipelines

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.

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Nurendra Choudhary Chandan Reddy

Nurendra Choudhary

Ph.D. student, Virginia Tech

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.



Karthik Subbian Chandan Reddy

Karthik Subbian

Principal Scientist, Amazon

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.

Srinivasan H. Sengamedu Chandan Reddy

Srinivasan H. Sengamedu

Senior Machine Learning Manager, Amazon

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.



Chandan Reddy Chandan Reddy

Chandan Reddy

Professor, Department of Computer Science, Virginia Tech

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.