Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models

• URL: http://arxiv.org/abs/2503.14411v1
• Date: Tue, 18 Mar 2025 16:50:10 GMT
• Title: Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
• Authors: Siwei Zhang, Yun Xiong, Yateng Tang, Xi Chen, Zian Jia, Zehao Gu, Jiarong Xu, Jiawei Zhang,
• Abstract summary: Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling.<n>We present textbfCross, a novel framework that seamlessly extends existing TGNNs for TTAG modeling.
• Score: 19.710059031046377
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed graphs (TTAGs). Such combination of dynamic text semantics and evolving graph structures introduces heightened complexity. Existing TGNNs embed texts statically and rely heavily on encoding mechanisms that biasedly prioritize structural information, overlooking the temporal evolution of text semantics and the essential interplay between semantics and structures for synergistic reinforcement. To tackle these issues, we present \textbf{{Cross}}, a novel framework that seamlessly extends existing TGNNs for TTAG modeling. The key idea is to employ the advanced large language models (LLMs) to extract the dynamic semantics in text space and then generate expressive representations unifying both semantics and structures. Specifically, we propose a Temporal Semantics Extractor in the {Cross} framework, which empowers the LLM to offer the temporal semantic understanding of node's evolving contexts of textual neighborhoods, facilitating semantic dynamics. Subsequently, we introduce the Semantic-structural Co-encoder, which collaborates with the above Extractor for synthesizing illuminating representations by jointly considering both semantic and structural information while encouraging their mutual reinforcement. Extensive experimental results on four public datasets and one practical industrial dataset demonstrate {Cross}'s significant effectiveness and robustness.

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