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Article type: Research Article
Authors: Hu, Mana | Sun, Dezhib; * | Bai, Yihana | Xiao, Hana | You, Fuchenga
Affiliations: [a] School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China | [b] School of Information Science and Engineering, Beijing City University, Beijing, China
Correspondence: [*] Corresponding author Dezhi Sun, School of Information Science and Engineering, Beijing City University, Beijing, China. E-mail: [email protected].
Abstract: In the realm of graph representation learning, Graph Neural Networks (GNNs) have demonstrated exceptional efficacy across diverse tasks. Typically, GNNs employ message-passing schemes to disseminate node features along graph structures, culminating in learned graph representations. However, their heavy reliance on smoothed node features over graph structures, coupled with limited expressiveness in the presence of node attributes, often constrains link prediction performance. To surmount this challenge, we propose GTLP, a Graph Transformer based link prediction framework. GTLP integrates unsupervised GNNs and structure encoding, enabling a holistic consideration of both topological structures and node features. This approach preserves critical node location and role information, enhancing the model’s expressiveness. By introducing the Graph Transformer model, GTLP adeptly incorporates neighbor information, refining embedding quality and bolstering the model’s learning and generalization capabilities. Notably, our method exhibits superior scalability, accommodating diverse techniques for information extraction, embedding learning, and sampling. Experimental results underscore GTLP’s state-of-the-art performance, outpacing various baselines across five real-world datasets.
Keywords: Deep learning, graph neural networks, graph transformer, link prediction
DOI: 10.3233/JIFS-237506
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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