HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

By Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha P. Talukdar. 2018

681 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8330c7c98c6c3e338ca578e0ab47f41bc18d0019

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Citations in the corpus

UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks (2021, methodology): “HyperGCN [Yadati et al., 2019] enhances the generalized hypergraph Laplacian with additional weighted pairwise edges (a.”
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, background): “Finally, there exists a new line of work focusing on extending GNNs to hypergraphs, see, e.g., (Bai et al., 2019; Yadati et al., 2019; Zhang et al., 2020), and a line of work in the data mining community incorporating global or higher-order information into graph or node embeddings, see, e.g., (Cao…”
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, background): “2019) and HyperGCN (Yadati et al. 2019) are the first to apply graph convolution to hypergraph.”
HGNN+: General Hypergraph Neural Networks (2022, methodology): “Seven typical state-of-the-art methods, including spectral-based (GCN [2], GraphConv [28]), non-spectral-based (GraphSAGE [1], GAT [3], and GIN [17]), and hypergraph-based methods (Hyper-Atten[7] and HyperGCN[8]), are selected for comparison.”
Adversarial Attack and Defense on Graph Data: A Survey (2018, result): “It has stimulated (and been cited by) various followingup novel research in this line [17], [57], [64], [130], [144] as well as other attempts of survey [25], [58].”
Language is All a Graph Needs (2023, background): “Overall, GNNs with various message passing mechanisms can be categorized as spatial-based ones (Hamilton et al., 2017; Veliˇckovi´c et al., 2017; Xu et al., 2018a; Monti et al., 2017) and spectral-based ones (Kipf and Welling, 2016; Defferrard et al., 2016; Yadati et al., 2019).”
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021, background): “We conclude this part by mentioning a large body of work on neural networks for hypergraphs, which subsume simplicial complexes (Yadati et al., 2019; Feng et al., 2019; Zhang et al., 2020; Yadati, 2020).”