Graph Convolutional Network Design for Node Classification Accuracy Improvement

Mohammad Abrar Shakil Sejan, Md Habibur Rahman, Md Abdul Aziz, Jung-In Baik, Young-Hwan You and Hyoung-Kyu Song ()
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Mohammad Abrar Shakil Sejan: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Md Habibur Rahman: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Md Abdul Aziz: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Jung-In Baik: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Young-Hwan You: Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
Hyoung-Kyu Song: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2023, vol. 11, issue 17, 1-13

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.

Keywords: graph data structure; graph learning; graph convolutional network; node classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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