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Hypergraph Neural Networks:
KAIST
Seoul, South Korea
University of Turin
Turin, Italy
KAIST
Seoul, South Korea
University of Turin
Turin, Italy
Tsinghua University
Beijing, China
KAIST
Seoul, South Korea
Summary
Summary
Higher-order interactions (HOIs) are ubiquitous in real-world networks, such as group discussions on online Q&A platforms, co-purchases of items in e-commerce, and collaborations of researchers. Investigation of deep learning for networks of HOIs, expressed as hypergraphs, has become an important agenda for the data mining and machine learning communities. As a result, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given this emerging trend, we provide a first and timely tutorial dedicated to HNNs. We cover the (1) inputs, (2) message-passing schemes, (3) training strategies, (4) applications (e.g., recommender systems and time series analysis), and (5) open problems of HNNs.
Target audience: This tutorial is intended for researchers and practitioners who are interested in representation learning for HOIs and its significant applications, including recommender systems, bioinformatics, time-series analysis, and computer vision.
Prerequisites: A preliminary understanding of graph representation learning would be beneficial.
Teaser Video
Teaser Video
Slides
Slides
Presenters
Presenters
Soo Yong Lee is a Ph.D. student at Kim Jaechul Graduate School of AI, KAIST, South Korea. He received his M.S. degree in artificial intelligence from KAIST in 2023 and B.S. degree in psychology from UCSD in 2016. His research interests include (hyper)graph representation learning and mining. His works have been published in major venues, including WWW, CIKM, ICML, and ICLR.
More information about Soo Yong can be found at https://github.com/syleeheal/.
Sunwoo Kim is a Ph.D. student at Kim Jaechul Graduate School of AI, KAIST, South Korea.} He received his M.S. degree in artificial intelligence from KAIST in 2024 and B.A. degree in applied statistics from Yonsei University in 2022. His research interests include hypergraph representation learning and mining. His works have been published in major venues, including KDD, ICDM, ICLR, and CVPR.
More information about Sunwoo can be found at https://sites.google.com/view/sunwoo97.
Yue Gao is a Tenured Associate Professor at the School of Software, Tsinghua University, China. He received his Ph.D. from the Department of Automation at Tsinghua University in 2012. His research falls in the fields of AI, CV, and brain science. He has published 100+ papers in premier conferences and journals, including KDD, CVPR, and TPAMI. He also serves as an editorial board member/associate editor for the International Journal of Computer Vision and Medical Image Analysis. He is a Senior Member of IEEE and a Member of ACM.
More information about Sunwoo can be found at https://www.thss.tsinghua.edu.cn/en/faculty/yuegao.
Alessia Antelmi is an Assistant Professor at the Department of Computer Science of the University of Turin, Italy.} She received her MSc and Ph.D. in Computer Science from the University of Salerno (Italy) in 2018 and 2022, respectively. In 2018, Alessia got a student travel grant to visit the Unit for Social Semantics, Data Science Institue (Galway, Ireland), led by Prof. John Breslin, and, in 2023, a research grant to join the NERDS research group at ITU to work on the COCOONS project led by Prof. Luca Maria Aiello. Her research primarily relates to exploiting hypergraphs to study the high-order relationships characterizing real-world phenomena, specifically focusing on online social influence diffusion and collective behavior.
More information about Alessia can be found at https://alessant.github.io/.
Mirko Polato is an Assistant Professor at the Department of Computer Science of the University of Turin, Italy. Mirko Polato received his Ph.D. in Brain, Mind, and Computer Science from the University of Padova (Italy) in 2018. In 2017, Dr. Polato was a visiting Ph.D. student at the Delft University of Technology in the Multimedia Computing group. From 2018 to 2021, he was a post-doctoral fellow at the University of Padova, working on two H2020 projects. Since 2021, he has been an Assistant Professor at the University of Turin, and his main research topics include federated learning, interpretable machine learning, hypergraph representation learning, and recommender systems. He co-organized Special Sessions and Workshops on many top venues, including WWW and IJCNN. He served as a Program Committee member of several international conferences and as a referee for several international journals. He authored about 50 research products, including international peer-reviewed conferences and journal papers.
More information about Mirko can be found at https://makgyver.github.io.
Kijung Shin is an Associate Professor jointly affiliated in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering at KAIST, South Korea.} He received his Ph.D. in Computer Science from Carnegie Mellon University (USA) in 2019. His research interests span various topics on data mining and machine learning for (hyper)graph-structured data, with a focus on scalable algorithm design and empirical patterns in the real world. He has published more than 80 referred articles in major data mining, database, and machine learning venues. He won the best research paper award at KDD 2016 and the best student paper runner-up award at ICDM 2023.
More information about Kijung can be found at https://kijungs.github.io.
Important References
Important References
D. Zhou, J. Huang, and B. Sch¨olkopf, “Learning with hypergraphs: Clustering, classification, and embedding,” in NeurIPS, 2006.
Y. Dong, W. Sawin, and Y. Bengio, “Hnhn: Hypergraph networks with hyperedge neurons,” in ICML Workshop: Graph Representation Learning and Beyond, 2020.
X. Xia, H. Yin, J. Yu, Q. Wang, L. Cui, and X. Zhang, “Self-supervised hypergraph convolutional networks for session-based recommendation,” in AAAI, 2021.
R. Sawhney, S. Agarwal, A. Wadhwa, T. Derr, and R. R. Shah, “Stock selection via spatiotemporal hypergraph attention network: A learning to rank approach,” in AAAI, 2021.
K. M. Saifuddin, B. Bumgardner, F. Tanvir, and E. Akbas, “Hygnn: Drug-drug interaction prediction via hypergraph neural network,” in ICDE, 2023.
X. Sun, H. Cheng, B. Liu, J. Li, H. Chen, G. Xu, and H. Yin, “Self-supervised hypergraph representation learning for sociological analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11860-11871, 2023.
S. Kim, S. Y. Lee, Y. Gao, A. Antelmi, M. Polato, and K. Shin, “A survey on hypergraph neural networks: an in-depth and step-by-step guide,” in KDD, 2024.
Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao, “Hypergraph neural networks,” in AAAI, 2019.
N. Yadati, M. Nimishakavi, P. Yadav, V. Nitin, A. Louis, and P. Talukdar, “Hypergcn: A new method for training graph convolutional networks on hypergraphs,” in NeurIPS, 2019.
E. Chien, C. Pan, J. Peng, and O. Milenkovic, “You are allset: A multiset function framework for hypergraph neural networks,” in ICLR, 2022.
C. Yang, R. Wang, S. Yao, and T. Abdelzaher, “Semi-supervised hypergraph node classification on hypergraph line expansion,” in CIKM, 2022.
M. Wang, Y. Zhen, Y. Pan, Z. Xu, R. Guo, and X. Zhao, “Tensorized hypergraph neural networks,” in SDM, 2024.
S. Kim, D. Lee, Y. Kim, J. Park, T. Hwang, and K. Shin, “Datasets, tasks, and training methods for large-scale hypergraph learning,” Data Mining and Knowledge Discovery, vol. 37, no. 6, pp. 2216-2254, 2023.
M. Choe, S. Kim, J. Yoo, and K. Shin, “Classification of edge-dependent labels of nodes in hypergraphs,” in KDD, 2023.
Y. Gao, Y. Feng, S. Ji, and R. Ji, “Hgnn+: General hypergraph neural networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3181-3199, 2022.
P. Wang, S. Yang, Y. Liu, Z. Wang, and P. Li, “Equivariant hypergraph diffusion neural operators,” in ICLR, 2023.
J. Bai, B. Gong, Y. Zhao, F. Lei, C. Yan, and Y. Gao, “Multi-scale representation learning on hypergraph for 3d shape retrieval and recognition,” IEEE Transactions on Image Processing, vol. 30, pp. 5327-5338, 2021.
D. Lee and K. Shin, “I’m me, we’re us, and i’m us: Tri-directional contrastive learning on hypergraphs,” in AAAI, 2023.
S. Kim, S. Kang, F. Bu, S. Y. Lee, J. Yoo, and K. Shin, “Hypeboy: Generative self-supervised representation learning on hypergraphs,” in ICLR, 2024.
H. Hwang, S. Lee, C. Park, and K. Shin, “Ahp: Learning to negative sample for hyperedge prediction,” in SIGIR, 2022.
F. Lei, J. Huang, J. Jiang, D. Huang, Z. Li, and C.-D. Wang, “Unveiling the potential of long-range dependence with mask-guided structure learning for hypergraph,” Knowledge-Based Systems, vol. 284, p. 111254, 2024.
Y. Li, C. Gao, H. Luo, D. Jin, and Y. Li, “Enhancing hypergraph neural networks with intent disentanglement for session-based recommendation,” in SIGIR, 2022.
C. Chen, C. Liao, and Y.-Y. Liu, “Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning,” Nature Communications, vol. 14, no. 1, p. 2375, 2023.
R. Sawhney, S. Agarwal, A. Wadhwa, and R. R. Shah, “Spatiotemporal hypergraph convolution network for stock movement forecasting,” in ICDM, 2020.
Y. Han, P. Wang, S. Kundu, Y. Ding, and Z. Wang, “Vision hgnn: An image is more than a graph of nodes,” in ICCV, 2023.
N. Yadati, V. Nitin, M. Nimishakavi, P. Yadav, A. Louis, and P. Talukdar, “Nhp: Neural hypergraph link prediction,” in CIKM, 2020.
Datasets
Datasets
HypergraphRepository: https://hypergraphrepository.di.unisa.it/
Dr. Austin Benson's hypergraph datasets: https://www.cs.cornell.edu/~arb/data/
Mr. Sunwoo Kim's large-scale hypergraph datasets: https://github.com/kswoo97/pcl
Mr. Sunwoo Kim's directed hypergraph datasets: https://github.com/kswoo97/hyprec
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