Abstract: HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their
potential in modeling high-order relations preserved in graph structured data.
However, most existing convolution filters are localized and determined by the
pre-defined initial hypergraph topology, neglecting to explore implicit and
long-ange relations in real-world data. In this paper, we propose the first
learning-based method tailored for constructing adaptive hypergraph structure,
termed HypERgrAph Laplacian aDaptor (HERALD), which serves as a generic
plug-in-play module for improving the representational power of HGCNNs.
Specifically, HERALD adaptively optimizes the adjacency relationship between
hypernodes and hyperedges in an end-to-end manner and thus the task-aware
hypergraph is learned. Furthermore, HERALD employs the self-attention mechanism
to capture the non-local paired-nodes relation. Extensive experiments on
various popular hypergraph datasets for node classification and graph
classification tasks demonstrate that our approach obtains consistent and
considerable performance enhancement, proving its effectiveness and