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WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks
Journal article   Open access   Peer reviewed

WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

Jinli Zhang, Zongli Jiang, Zheng Chen and Xiaohua Hu
IEEE access, v 8, pp 40744-40754
2020
DOI: https://doi.org/10.1109/ACCESS.2020.2977332
url
https://doi.org/10.1109/access.2020.2977332View
Published, Version of Record (VoR)CC BY V4.0,  Open
url
https://doi.org/10.1109/ACCESS.2020.2977332View
Published, Version of Record (VoR) Open

Abstract

Convolution graph convolutional network Heterogeneous network Heterogeneous networks Licenses Neural networks Predictive models representation learning Semantics Task analysis weighted meta-graph
Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for improvement. Random walks based methods are currently popular methods to learn network embedding; however, they are random and limited by the length of sampled walks, and have difficulty capturing network structural information. Some recent researches proposed using meta paths to express the sample relationship in HNs. Another popular graph learning model, the graph convolutional network (GCN) is known to be capable of better exploitation of network topology, but the current design of GCN is intended for homogenous networks. This paper proposes a novel combination of meta-graph and graph convolution, the meta-graph based graph convolutional networks (MGCN). To fully capture the complex long semantic information, MGCN utilizes different meta-graphs in HNs. As different meta-graphs express different semantic relationships, MGCN learns the weights of different meta-graphs to make up for the loss of semantics when applying GCN. In addition, we improve the current convolution design by adding node self-significance. To validate our model in learning feature representation, we present comprehensive experiments on four real-world datasets and two representation tasks: classification and link prediction. WMGCN's representations can improve accuracy scores by up to around 10% in comparison to other popular representation learning models. What's more, WMGCN'feature learning outperforms other popular baselines. The experimental results clearly show our model is superior over other state-of-the-art representation learning algorithms.

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4 citations in Web of Science
6 citations in Scopus

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Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
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