Zhang, Zhihong, Chen, Dongdong, Wang, Zeli et al. (3 more authors) (2019) Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning. Pattern Recognition. pp. 363-376. ISSN 0031-3203
Abstract
Network representation learning (NRL) aims to map vertices of a network into a low-dimensional space which preserves the network structure and its inherent properties. Most existing methods for network representation adopt shallow models which have relatively limited capacity to capture highly non-linear network structures, resulting in sub-optimal network representations. Therefore, it is nontrivial to explore how to effectively capture highly non-linear network structure and preserve the global and local structure in NRL. To solve this problem, in this paper we propose a new graph convolutional autoencoder architecture based on a depth-based representation of graph structure, referred to as the depth-based subgraph convolutional autoencoder (DS-CAE), which integrates both the global topological and local connectivity structures within a graph. Our idea is to first decompose a graph into a family of K-layer expansion subgraphs rooted at each vertex aimed at better capturing long-range vertex inter-dependencies. Then a set of convolution filters slide over the entire sets of subgraphs of a vertex to extract the local structural connectivity information. This is analogous to the standard convolution operation on grid data. In contrast to most existing models for unsupervised learning on graph-structured data, our model can capture highly non-linear structure by simultaneously integrating node features and network structure into network representation learning. This significantly improves the predictive performance on a number of benchmark datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. https://authors.elsevier.com/c/1YYcI77nKWPC2 Elsevier 50 day share link available until 02/04/2019 |
Keywords: | Graph convolutional neural network,Network representation learning,Node classification |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 14 Jan 2019 16:00 |
Last Modified: | 02 Apr 2025 23:14 |
Published Version: | https://doi.org/10.1016/j.patcog.2019.01.045 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.patcog.2019.01.045 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140982 |