[1]CHANG Xingong,WANG Jinjue.Network representation learning using graph convolution ensemble[J].CAAI Transactions on Intelligent Systems,2022,17(3):547-555.[doi:10.11992/tis.202107048]
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Network representation learning using graph convolution ensemble

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 547-555 Column: 学术论文—智能系统 Public date: 2022-05-05
Title:
Network representation learning using graph convolution ensemble
Author(s):
CHANG Xingong;  WANG Jinjue
School of Information, Shanxi University of Finance and Economics, Taiyuan 030006, China
Keywords:
Network represents learning; Ensemble learning; Graph convolution network; Social network; Deep learning; Feature learning; Node embedding; Information network embedding
CLC:
TP301.6
DOI:
10.11992/tis.202107048
Abstract:
Aimed at the weak generalization ability of the existing network representation learning methods, this paper proposes a stacking ensemble method that is applied to network representation learning to improve the performance of network representation. Three classical shallow network representation learning methods, namely, DeepWalk, Node2Vec, and Line, are used initially as parallel primary learners. After training, the embedded representation and three spliced node parts are obtained. Following this, the graph convolutional network (GCN) is selected as the secondary learner to stack and integrate the network structure and the new data set. This is done to obtain the final node embedded representation. GCN efficiently deals with semisupervised classification problems. As network representation learning is unsupervised, the first-order proximity of the network has been used to design the loss function. Finally, evaluation indices are designed to test the primary learner and integrated node eigenvector representation. The experimental results show that after the introduction of the integration method, each index improves by approximately 1.47–2.97 times.
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