TGSBM: Transformer-Guided Stochastic Block Model for Link Prediction

• URL: http://arxiv.org/abs/2601.20646v1
• Date: Wed, 28 Jan 2026 14:32:24 GMT
• Title: TGSBM: Transformer-Guided Stochastic Block Model for Link Prediction
• Authors: Zhejian Yang, Songwei Zhao, Zilin Zhao, Hechang Chen,
• Abstract summary: Link prediction is a cornerstone of the Web ecosystem, powering applications from recommendation and search to knowledge graph completion and collaboration forecasting.<n>Existing approaches face notable limitations: traditional graph neural networks struggle to capture global dependencies, while recent graph transformers achieve strong performance but incur lack of interpretable structural structure.<n>We propose text-Guided Block Model, a framework that integrates the principled generative structure of Overlapping Block Models with the power of sparse Graph Transformers.
• Score: 13.840265247620556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Link prediction is a cornerstone of the Web ecosystem, powering applications from recommendation and search to knowledge graph completion and collaboration forecasting. However, large-scale networks present unique challenges: they contain hundreds of thousands of nodes and edges with heterogeneous and overlapping community structures that evolve over time. Existing approaches face notable limitations: traditional graph neural networks struggle to capture global structural dependencies, while recent graph transformers achieve strong performance but incur quadratic complexity and lack interpretable latent structure. We propose \textbf{TGSBM} (Transformer-Guided Stochastic Block Model), a framework that integrates the principled generative structure of Overlapping Stochastic Block Models with the representational power of sparse Graph Transformers. TGSBM comprises three main components: (i) \emph{expander-augmented sparse attention} that enables near-linear complexity and efficient global mixing, (ii) a \emph{neural variational encoder} that infers structured posteriors over community memberships and strengths, and (iii) a \emph{neural edge decoder} that reconstructs links via OSBM's generative process, preserving interpretability. Experiments across diverse benchmarks demonstrate competitive performance (mean rank 1.6 under HeaRT protocol), superior scalability (up to $6\times$ faster training), and interpretable community structures. These results position TGSBM as a practical approach that strikes a balance between accuracy, efficiency, and transparency for large-scale link prediction.

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