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Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks.
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020 · 1907
Earlier work this paper cites.
Higher Order Learning with Graphs. In Proceedings of the 23rd International Conference on Machine Learning (Pittsburgh, Pennsylvania, USA)
Sameer Agarwal, Kristin Branson, and Serge Belongie. 2006 · 2006
Earlier work this paper cites.
HNHN: Hypergraph Networks with Hyperedge Neurons
Yihe Dong, Will Sawin, and Yoshua Bengio. 2020 · 2006
Earlier work this paper cites.
Hypergraph spectral learning for multi-label classification. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Liang Sun, Shuiwang Ji, and Jieping Ye. 2008 · 2008
Earlier work this paper cites.
Hypergraphs and Cellular Networks
Steffen Klamt, Utz-Uwe Haus, and Fabian Theis. 2009 · 2009
Earlier work this paper cites.
HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs
Devanshu Arya, Deepak K. Gupta, Stevan Rudinac, and Marcel Worring. 2020 · 2010
Earlier work this paper cites.
Scalable Simple Random Sampling and Stratified Sampling. In Proceedings of the 30th International Conference on Machine Learning
Xiangrui Meng. 2013 · 2013
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Exploiting Relational Information in Social Networks using Geometric Deep Learning on Hypergraphs (ICMR ’18) . 117–125
Devanshu Arya and Marcel Worring. 2018 · 2018
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
Adaptive Sampling Towards Fast Graph Representation Learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018 · 2018
Earlier work this paper cites.
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research)
Pan Li and Olgica Milenkovic. 2018 · 2018
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities. In Proceedings of the 27th ACM International Conference on Multimedia . Association for Computing Machinery, New York, NY, USA
Devanshu Arya, Stevan Rudinac, and Marcel Worring. 2019 · 2019
Earlier work this paper cites.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19) . ACM
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
Earlier work this paper cites.
H S 2 HS^{2} : Active learning over hypergraphs with pointwise and pairwise queries. In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research)
I (Eli) Chien, Huozhi Zhou, and Pan Li. 2019 · 2019
Earlier work this paper cites.
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research) . 1172–1181
Uthsav Chitra and Benjamin Raphael. 2019 · 2019
Cited alongside, same era.
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019 · 2019
Cited alongside, same era.
Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds
Matthias Fey and Jan E. Lenssen. 2019 · 2019
Cited alongside, same era.
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. 2019 · 2019
Cited alongside, same era.
Visual Analytics for Temporal Hypergraph Model Exploration
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks. In International Conference on Learning Representations
Eli Chien, Chao Pan, Jianhao Peng, and Olgica Milenkovic. 2022 · 2022
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Bayesian Structure Learning with Generative Flow Networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio. 2022 · 2022
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A Survey on Hypergraph Representation Learning
Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, and Dingqi Yang. 2023 · 2023
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Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, and Emmanuel Bengio. 2023 · 2023
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A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, and Zhangyang Wang. 2023 · 2023
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Maximilian T. Fischer, Devanshu Arya, Dirk Streeb, Daniel Seebacher, Daniel A. Keim, and Marcel Worring. 2021 · 2020
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Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. 2020 · 2020
Cited alongside, same era.
Hypergraph learning with line expansion
Chaoqi Yang, Ruijie Wang, Shuochao Yao, and Tarek Abdelzaher. 2020 · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020 · 2020
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Adaptive Neural Message Passing for Inductive Learning on Hypergraphs
Devanshu Arya, Deepak K. Gupta, Stevan Rudinac, and Marcel Worring. 2021 · 2021
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Hypergraph convolution and hypergraph attention
Song Bai, Feihu Zhang, and Philip H.S. Torr. 2021 · 2021
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Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio. 2021 · 2021
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UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks
Jing Huang and Jie Yang. 2021 · 2021
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MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah. 2023 · 2023
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GFlowNet-EM for learning compositional latent variable models
Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie Everett, Alexandros Graikos, and Yoshua Bengio. 2023 · 2023
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Datasets, tasks, and training methods for large-scale hypergraph learning
Sunwoo Kim, Dongjin Lee, Yul Kim, Jungho Park, Taeho Hwang, and Kijung Shin. 2023 · 2023
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Learning GFlowNets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, and Nikolay Malkin. 2023 · 2023
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Trajectory balance: Improved credit assignment in GFlowNets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio. 2023 · 2023
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Active Learning for Multilingual Fingerspelling Corpora
Shuai Wang and Eric Nalisnick. 2023 · 2023
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GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Thiviyan Thanapalasingam, Emile van Krieken, Daniel Daza, and Peter Bloem. 2023 · 2023
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High-performance computing in healthcare: an automatic literature analysis perspective
Jieyi Li, Shuai Wang, Stevan Rudinac, and Anwar Osseyran. 2024 · 2024
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Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks. In MultiMedia Modeling
Shuai Wang, Jiayi Shen, Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, and Marcel Worring. 2024 · 2024
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Hongyi Zhu, Jia-Hong Huang, Stevan Rudinac, and Evangelos Kanoulas. 2024 · 2024
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