Heterogeneous Hyperedge Convolutional Network |
| |
Authors: | Yong Wu Binjun Wang Wei Li |
| |
Affiliation: | 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. |
| |
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. |
| |
Keywords: | Graph convolutional networks heterogeneous information networks metastructure |
|
| 点击此处可从《》浏览原始摘要信息 |
|
点击此处可从《》下载全文 |
|