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Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes

Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes
Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes
Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.
2159-5399
5892-5899
AAAI Press
Sun, Ke
13d9d51e-b3f1-43db-83a4-0b6ec16bd899
Lin, Zhouchen
83875401-acfb-4063-8370-d563b878fe6c
Zhu, Zhanxing
e55e7385-8ba2-4a85-8bae-e00defb7d7f0
Sun, Ke
13d9d51e-b3f1-43db-83a4-0b6ec16bd899
Lin, Zhouchen
83875401-acfb-4063-8370-d563b878fe6c
Zhu, Zhanxing
e55e7385-8ba2-4a85-8bae-e00defb7d7f0

Sun, Ke, Lin, Zhouchen and Zhu, Zhanxing (2020) Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). vol. 34, AAAI Press. pp. 5892-5899 . (doi:10.1609/aaai.v34i04.6048).

Record type: Conference or Workshop Item (Paper)

Abstract

Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.

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More information

Published date: 3 April 2020
Venue - Dates: The Thirty-Fourth AAAI Conference on Artificial Intelligence, Hilton Midtown New York, New York, United States, 2020-02-07 - 2020-02-12

Identifiers

Local EPrints ID: 486294
URI: http://eprints.soton.ac.uk/id/eprint/486294
ISSN: 2159-5399
PURE UUID: c084ed1f-4199-46c8-9116-5a4cb476ae21

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Date deposited: 16 Jan 2024 17:52
Last modified: 14 Jun 2024 17:25

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Contributors

Author: Ke Sun
Author: Zhouchen Lin
Author: Zhanxing Zhu

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