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Graph neural network (GNN) has gained increasing popularity in recent yearsowing to its capability and flexibility in modeling complex graph structuredata. Among all graph learning methods, hypergraph learning is a technique forexploring the implicit higher-order correlations when training the embeddingspace of the graph. In this paper, we propose a hypergraph learning frameworknamed LFH that is capable of dynamic hyperedge construction and attentiveembedding update utilizing the heterogeneity attributes of the graph.Specifically, in our framework, the high-quality features are first generatedby the pairwise fusion strategy that utilizes explicit graph structureinformation when generating initial node embedding. Afterwards, a hypergraph isconstructed through the dynamic grouping of implicit hyperedges, followed bythe type-specific hypergraph learning process. To evaluate the effectiveness ofour proposed framework, we conduct comprehensive experiments on several populardatasets with eleven state-of-the-art models on both node classification andlink prediction tasks, which fall into categories of homogeneous pairwise graphlearning, heterogeneous pairwise graph learning, and hypergraph learning. Theexperiment results demonstrate a significant performance gain (average 12.5% innode classification and 13.3% in link prediction) compared with recentstate-of-the-art methods.

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs