第一篇 Edge Representation Learning with Hypergraphs Abstract 图神经网络最近在表示图结构数据方面取得了显著的成功,在节点嵌入和图的池化方法方面都取得了迅速的进展。考虑到节点的连通性,过往的工作主要关注于从节点中获取信息,而在edge的representation方面没有做太多工作,这是图的基本组成部分。然而,对于诸如图重构...
在图神经网络（GNN）取得显著进展的同时，对于边缘（edge）的表示学习却相对较少关注。传统的GNN主要集中在节点的表示学习上，而边缘在图重构和生成等任务中的关键作用并未充分挖掘。为此，我们提出了一种创新的边缘表示学习框架，称为双超图变换（DHT）的边缘超图神经网络（EHGNN）。DHT将图的边缘转换为...
If you found the provided code with our paper useful in your work, we kindly request that you cite our work. @inproceedings{jo2021ehgnn,author={Jaehyeong Jo andJinheon Baek andSeul Lee andDongki Kim andMinki Kang andSung Ju Hwang},title={Edge Representation Learning with Hypergraphs},book...
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Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization schem...
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization schem...
the importance of the neighbours in each hyperedge is multiplied. While the two models coincide on standard graphs (as each hyperedge involves exactly two nodes, and hence there is at most one neighbour), these two models are intrinsically different on uniform hypergraphs with larger hyperedges....
In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes contribute. It can be viewed as a generalization of both message passing neural networks and Transformers. We evaluate HEAT on knowl...
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization schem...
Learning with hypergraphs: Clustering, classification and embedding. In Proceedings of the 19th International Conference on Neural Information Processing Systems, Vancouver, Canada, 4–7 December 2006; pp. 1601–1608. [Google Scholar] Sharma, K.K.; Seal, A.; Herrera-Viedma, E.; Krejcar, O. ...