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Multigraph Fusion for Dynamic Graph Convolutional Network | IEEE Journals & Magazine | IEEE Xplore

Multigraph Fusion for Dynamic Graph Convolutional Network


Abstract:

Graph convolutional network (GCN) outputs powerful representation by considering the structure information of the data to conduct representation learning, but its robustn...Show More

Abstract:

Graph convolutional network (GCN) outputs powerful representation by considering the structure information of the data to conduct representation learning, but its robustness is sensitive to the quality of both the feature matrix and the initial graph. In this article, we propose a novel multigraph fusion method to produce a high-quality graph and a low-dimensional space of original high-dimensional data for the GCN model. Specifically, the proposed method first extracts the common information and the complementary information among multiple local graphs to obtain a unified local graph, which is then fused with the global graph of the data to obtain the initial graph for the GCN model. As a result, the proposed method conducts the graph fusion process twice to simultaneously learn the low-dimensional space and the intrinsic graph structure of the data in a unified framework. Experimental results on real datasets demonstrated that our method outperformed the comparison methods in terms of classification tasks.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 35, Issue: 1, January 2024)
Page(s): 196 - 207
Date of Publication: 16 May 2022

ISSN Information:

PubMed ID: 35576414

Funding Agency:


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