Yong Wu1, Binjun Wang1, *, Wei Li2
CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2277-2294, 2020, DOI:10.32604/cmc.2020.011609
Abstract Graph convolutional networks (GCNs) have been developed as a general and
powerful tool to handle various tasks related to graph data. However, current methods
mainly consider homogeneous networks and ignore the rich semantics and multiple types
of objects that are common in heterogeneous information networks (HINs). In this paper,
we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph
convolutional network architecture that operates on HINs. Specifically, we extract the
rich semantics by different metastructures and adopt hyperedge to model the interactions
among metastructure-based neighbors. Due to the powerful information extraction
capabilities of metastructure and hyperedge, HHCN has the… More >
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