Unlocking Multi-Modal Potentials for Link Prediction on Dynamic Text-Attributed Graphs

• URL: http://arxiv.org/abs/2502.19651v2
• Date: Fri, 01 Aug 2025 10:07:08 GMT
• Title: Unlocking Multi-Modal Potentials for Link Prediction on Dynamic Text-Attributed Graphs
• Authors: Yuanyuan Xu, Wenjie Zhang, Ying Zhang, Xuemin Lin, Xiwei Xu,
• Abstract summary: Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal events (edges) alongside rich textual attributes.<n>MoMent is a multi-modal model that explicitly models, integrates, and aligns each modality to learn node representations for link prediction.<n>Experiments show that MoMent achieves up to 17.28% accuracy improvement and up to 31x speed-up against eight baselines.
• Score: 28.533930417703715
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal events (edges) alongside rich textual attributes. Existing studies can be broadly categorized into TGNN-driven and LLM-driven approaches, both of which encode textual attributes and temporal structures for DyTAG representation. We observe that DyTAGs inherently comprise three distinct modalities: temporal, textual, and structural, often exhibiting completely disjoint distributions. However, the first two modalities are largely overlooked by existing studies, leading to suboptimal performance. To address this, we propose MoMent, a multi-modal model that explicitly models, integrates, and aligns each modality to learn node representations for link prediction. Given the disjoint nature of the original modality distributions, we first construct modality-specific features and encode them using individual encoders to capture correlations across temporal patterns, semantic context, and local structures. Each encoder generates modality-specific tokens, which are then fused into comprehensive node representations with a theoretical guarantee. To avoid disjoint subspaces of these heterogeneous modalities, we propose a dual-domain alignment loss that first aligns their distributions globally and then fine-tunes coherence at the instance level. This enhances coherent representations from temporal, textual, and structural views. Extensive experiments across seven datasets show that MoMent achieves up to 17.28% accuracy improvement and up to 31x speed-up against eight baselines.

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