Retrieval Augmented Generation for Dynamic Graph Modeling

• URL: http://arxiv.org/abs/2408.14523v1
• Date: Mon, 26 Aug 2024 09:23:35 GMT
• Title: Retrieval Augmented Generation for Dynamic Graph Modeling
• Authors: Yuxia Wu, Yuan Fang, Lizi Liao,
• Abstract summary: Dynamic graph modeling is crucial for analyzing evolving patterns in various applications. Existing approaches often integrate graph neural networks with temporal modules or redefine dynamic graph modeling as a generative sequence task. We introduce the Retrieval-Augmented Generation for Dynamic Graph Modeling (RAG4DyG) framework, which leverages guidance from contextually and temporally analogous examples.
• Score: 15.09162213134372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic graph modeling is crucial for analyzing evolving patterns in various applications. Existing approaches often integrate graph neural networks with temporal modules or redefine dynamic graph modeling as a generative sequence task. However, these methods typically rely on isolated historical contexts of the target nodes from a narrow perspective, neglecting occurrences of similar patterns or relevant cases associated with other nodes. In this work, we introduce the Retrieval-Augmented Generation for Dynamic Graph Modeling (RAG4DyG) framework, which leverages guidance from contextually and temporally analogous examples to broaden the perspective of each node. This approach presents two critical challenges: (1) How to identify and retrieve high-quality demonstrations that are contextually and temporally analogous to dynamic graph samples? (2) How can these demonstrations be effectively integrated to improve dynamic graph modeling? To address these challenges, we propose RAG4DyG, which enriches the understanding of historical contexts by retrieving and learning from contextually and temporally pertinent demonstrations. Specifically, we employ a time- and context-aware contrastive learning module to identify and retrieve relevant cases for each query sequence. Moreover, we design a graph fusion strategy to integrate the retrieved cases, thereby augmenting the inherent historical contexts for improved prediction. Extensive experiments on real-world datasets across different domains demonstrate the effectiveness of RAG4DyG for dynamic graph modeling.

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