Preventing Over-Smoothing for Hypergraph Neural Networks. (arXiv:2203.17159v2 [cs.LG] UPDATED)

In recent years, hypergraph learning has attracted great attention due to its
capacity in representing complex and high-order relationships. However, current
neural network approaches designed for hypergraphs are mostly shallow, thus
limiting their ability to extract information from high-order neighbors. In
this paper, we show both theoretically and empirically, that the performance of
hypergraph neural networks does not improve as the number of layers increases,
which is known as the over-smoothing problem. To avoid this issue, we develop a
new …

arxiv hypergraph networks neural networks