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Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated theirpotential in modeling high-order relations preserved in graph-structured data.However, most existing convolution filters are localized and determined by thepre-defined initial hypergraph topology, neglecting to explore implicit andlong-range relations in real-world data. In this paper, we propose the firstlearning-based method tailored for constructing adaptive hypergraph structure,termed HypERgrAph Laplacian aDaptor (HERALD), which serves as a genericplug-and-play module for improving the representational power ofHGCNNs.Specifically, HERALD adaptively optimizes the adjacency relationshipbetween vertices and hyperedges in an end-to-end manner and thus the task-awarehypergraph is learned. Furthermore, HERALD employs the self-attention mechanismto capture the non-local paired-nodes relation. Extensive experiments onvarious popular hypergraph datasets for node classification and graphclassification tasks demonstrate that our approach obtains consistent andconsiderable performance enhancement, proving its effectiveness andgeneralization ability.

Learnable Hypergraph Laplacian for Hypergraph Learning