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Graph Convolutional Network Design for Node Classification Accuracy Improvement

Author

Listed:
  • Mohammad Abrar Shakil Sejan

    (Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

  • Md Habibur Rahman

    (Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

  • Md Abdul Aziz

    (Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

  • Jung-In Baik

    (Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

  • Young-Hwan You

    (Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
    Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • Hyoung-Kyu Song

    (Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
    Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

Abstract

Graph convolutional networks (GCNs) provide an advantage in node classification tasks for graph-related data structures. In this paper, we propose a GCN model for enhancing the performance of node classification tasks. We design a GCN layer by updating the aggregation function using an updated value of the weight coefficient. The adjacency matrix of the input graph and the identity matrix are used to calculate the aggregation function. To validate the proposed model, we performed extensive experimental studies with seven publicly available datasets. The proposed GCN layer achieves comparable results with the state-of-the-art methods. With one single layer, the proposed approach can achieve superior results.

Suggested Citation

  • Mohammad Abrar Shakil Sejan & Md Habibur Rahman & Md Abdul Aziz & Jung-In Baik & Young-Hwan You & Hyoung-Kyu Song, 2023. "Graph Convolutional Network Design for Node Classification Accuracy Improvement," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3680-:d:1225779
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