Open Access

ARTICLE

Heterogeneous Hyperedge Convolutional Network

by Yong Wu, Binjun Wang, Wei Li

1 School of Information Technology and Cyber Security, People’s Public Security University of China, Beijing, 100038, China.
2 School of Chemistry and Physics, Queensland University of Technology, Brisbane, Queensland, 4001, Australia.

* Corresponding Author: Binjun Wang. Email: .

Computers, Materials & Continua 2020, 65(3), 2277-2294. https://doi.org/10.32604/cmc.2020.011609

Received 21 May 2020; Accepted 03 July 2020; Issue published 16 September 2020

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 flexibility to model the complex relationships in HINs by setting different combinations of metastructures and hyperedges. Moreover, a metastructure attention layer is also designed to allow each node to select the metastructures based on their importance and provide potential interpretability for graph analysis. As a result, HHCN can encode node features, metastructure-based semantics and hyperedge information simultaneously by aggregating features from metastructure-based neighbors in a hierarchical manner. We evaluate HHCN by applying it to the semi-supervised node classification task. Experimental results show that HHCN outperforms state-of-the-art graph embedding models and recently proposed graph convolutional network models.

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Citations