Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs

• URL: http://arxiv.org/abs/2507.03947v2
• Date: Wed, 30 Jul 2025 12:47:25 GMT
• Title: Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
• Authors: Thanh Hoang-Minh,
• Abstract summary: We focus on KBGAT, a graph neural network model that leverages multi-head attention to jointly encode both entity and relation features within local neighborhood structures.<n>We introduce textbfGCAT (Graph Collaborative Attention Network), a refined model that enhances context aggregation and interaction between heterogeneous nodes.<n>Our findings highlight the advantages of attention-based architectures in capturing complex relational patterns for knowledge graph completion tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs offer a structured representation of real-world entities and their relationships, enabling a wide range of applications from information retrieval to automated reasoning. In this paper, we conduct a systematic comparison between traditional rule-based approaches and modern deep learning methods for link prediction. We focus on KBGAT, a graph neural network model that leverages multi-head attention to jointly encode both entity and relation features within local neighborhood structures. To advance this line of research, we introduce \textbf{GCAT} (Graph Collaborative Attention Network), a refined model that enhances context aggregation and interaction between heterogeneous nodes. Experimental results on four widely-used benchmark datasets demonstrate that GCAT not only consistently outperforms rule-based methods but also achieves competitive or superior performance compared to existing neural embedding models. Our findings highlight the advantages of attention-based architectures in capturing complex relational patterns for knowledge graph completion tasks.

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