Multigraph Fusion for Dynamic Graph Convolutional Network

doi:10.1109/TNNLS.2022.3172588

Multigraph Fusion for Dynamic Graph Convolutional Network

2024-01-01

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DOI：10.1109/TNNLS.2022.3172588

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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.

Keywords:

Representation learning

Data models

Termination of employment

Learning systems

Task analysis

Robustness

Computer science

Data fusion

dimensionality reduction

graph convolutional networks (GCNs)

graph learning

Multigraph Fusion for Dynamic Graph Convolutional Network

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