Hypergaph Neural Networks - mgao97/Higher-Order-Network GitHub Wiki

[HGNN] Feng Y, You H, Zhang Z, et al. Hypergraph neural networks[C]//Proceedings of the AAAI conference on artificial intelligence (AAAI). 2019, 33(01): 3558-3565. HGNN Core contribution: propose spectral convolution on hyperedge by the node-hyperedge-node transform

[hypergcn_attention] Bai S, Zhang F, Torr P H S. Hypergraph convolution and hypergraph attention[J]. Pattern Recognition, 2021, 110: 107637. hypergcn_attention Core contribution: applying attention in the hyperedge convolution patterns

[HGNN+] Gao Y, Feng Y, Ji S, et al. HGNN $^+ $: General Hypergraph Neural Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022. HGNN+ Core contribution: The expansion version of HGNN with adaptively construction of hypergraphs and with a uniform framework to aggregate the way of convolution on hypergraphs.

[HpLapGCN] Fu S, Liu W, Zhou Y, et al. HpLapGCN: Hypergraph p-Laplacian graph convolutional networks[J]. Neurocomputing, 2019, 362: 166-174. HpLapGCN

[HyperDNE] Huang J, Lu T, Zhou X, et al. HyperDNE: Enhanced Hypergraph Neural Network for Dynamic Network Embedding[J]. Neurocomputing, 2023. HyperDNE

[IHGC-GAN] Bi X, Li L, Wang Z, et al. IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data[J]. Briefings in Bioinformatics, 2022, 23(3): bbac093. IHGC-GAN

[HSL] Cai D, Song M, Sun C, et al. Hypergraph structure learning for hypergraph neural networks[C]//Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI), Lud De Raedt, Ed. 2022, 7: 1923-1929. HSL Core contribution: propose a hypergraph structure learning framework adopting two-stage sampling process (hyperedge sampling and incident node sampling) and use HGNN model to make hypergraph learning for downstream tasks.

[RAHG] Li K, Huang Z, Jia Z. RAHG: A Role-Aware Hypergraph Neural Network for Node Classification in Graphs[J]. IEEE Transactions on Network Science and Engineering (TNSE), 2023. RAHG

[HGTN] Li M, Zhang Y, Li X, et al. Hypergraph Transformer Neural Networks[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2022. HGTN

[HHGNN] Li Y, Fan Z, Zhang J, et al. Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM). 2022: 4209-4213. HHGNN Code(https://github.com/liyongkang123/HHGNN) Core idea: Heterogeneous Hypergraph Convolution & Supervised Contrastive Learning

[FC-HAT] Ji J, Ren Y, Lei M. FC–HAT: Hypergraph attention network for functional brain network classification[J]. Information Sciences, 2022, 608: 1301-1316. FC-HAT

[HCCF] Xia L, Huang C, Xu Y, et al. Hypergraph contrastive collaborative filtering[C]//Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval (SIGIR). 2022: 70-79. HCCF

[ST-HSL] Li Z, Huang C, Xia L, et al. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction[C]//2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022: 2984-2996. ST-HSL

[SA-HyperGAT] Dong D, Lin F, Li G, et al. Sentiment-Aware Fake News Detection on Social Media with Hypergraph Attention Networks[C]//2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022: 2174-2180. SA-HyperGAT

[HyperGRL] Du B, Yuan C, Barton R, et al. Self-supervised Hypergraph Representation Learning[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 505-514. HyperGRL

[DyHCN] Yin N, Feng F, Luo Z, et al. Dynamic hypergraph convolutional network[C]//2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022: 1621-1634. DyHCN

[HashNWalk] Lee G, Choe M, Shin K. Hashnwalk: Hash and random walk based anomaly detection in hyperedge streams[J]. arXiv preprint arXiv:2204.13822, 2022. HashNWalk

[Madis] Choe M, Yoo J, Lee G, et al. Midas: Representative sampling from real-world hypergraphs[C]//Proceedings of the ACM Web Conference 2022. 2022: 1080-1092. Madis

[DHCN] Xia X, Yin H, Yu J, et al. Self-supervised hypergraph convolutional networks for session-based recommendation[C]//Proceedings of the AAAI conference on artificial intelligence (AAAI). 2021, 35(5): 4503-4511. DHCN

[RecQ] Yu J, Yin H, Li J, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]//Proceedings of the web conference (WWW). 2021: 413-424. RecQ

[HyperGCL] Wei T, You Y, Chen T, et al. Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative[C]//Advances in Neural Information Processing Systems (NeurIPS). 2022. HyperGCL

[LE] Yang C, Wang R, Yao S, et al. Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM). 2022: 2352-2361. LE

[Hyper-SAGNN] Zhang R, Zou Y, Ma J. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs[C]//International Conference on Learning Representations (ICLR). 2020. Hyper-SAGNN Code(https://github.com/ma-compbio/Hyper-SAGNN)

[DHNE] Tu K, Cui P, Wang X, et al. Structural deep embedding for hyper-networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 2018, 32(1). DHNE Code(https://github.com/tadpole/DHNE) Core contribution: The expansion of SDNE model on hypergraph with auto-encoder model and the fully connected layer to measure the tuple-similarity.