Title: Leveraging similarity and structural correlation for attention-based graph embedding

Authors: Jinghong Wang; Changxin Li; Jiateng Yang

Addresses: College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, 050024, China; Hebei Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, 050024, China; Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Security, Hebei Normal University, Shijiazhuang, 050024, China ' College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, 050024, China; Hebei Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, 050024, China; Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Security, Hebei Normal University, Shijiazhuang, 050024, China ' Hebei Polytechnic Institute, Shijiazhuang 050020, China

Abstract: Graph attention networks (GATs) are a prevalent method for graph embedding, utilising attention mechanisms to aggregate first-order neighbourhood node information. However, their inability to adequately consider high-order neighbourhood nodes and structural information poses limitations. To address this gap, we propose a novel joint attention graph embedding model integrating similar networks and structural correlation. By introducing the concept of structural correlation, our model comprehensively incorporates both node content features and joint node topological structure features when computing attention scores. Experimental results showcase the efficacy of our approach, yielding significant accuracy improvements of 2.70%, 3.94%, and 2.60% on the Cora, Citeseer, and Pubmed datasets, respectively, compared to traditional GATs. Our proposed method significantly enhances node embedding representation, underscoring its importance in improving performance in node classification tasks.

Keywords: graph embedding; graph attention network; GAT; node similarity; similar network; node classification.

DOI: 10.1504/IJBIC.2025.146914

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.4, pp.259 - 268

Received: 30 Nov 2023
Accepted: 05 Jun 2024
Published online: 26 Jun 2025 *

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