  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
generalization ability.
