Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression
arXiv:2601.08187v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding. Recent studies typically focus on verbalizing the graph structures via handcrafted prompts, feeding the target node and its neighborhood context into LLMs. However, constrained by the context window, existing methods mainly resort to random sampling, often implemented via dropping node/edge randomly, which inevitably introduces noise and cause reasoning instability. We argue that graphs inherently contain rich structural and sem
The continuous drive to enhance LLM performance and overcome context window limitations necessitates innovative approaches to graph data processing.
Improving how LLMs process complex graph structures and semantic information directly impacts their reasoning capabilities, making them more effective in understanding intricate real-world relationships.
This research outlines a method to mitigate instability and noise in LLM graph understanding, leading to more robust and reliable AI agent performance.
- · AI Agent developers
- · LLM researchers
- · Data scientists
- · Graph database providers
- · LLM implementations reliant on naive sampling
LLMs demonstrate improved accuracy and stability when processing graph-structured data.
More sophisticated AI agents emerge that can navigate and reason about highly interconnected information more effectively.
This could accelerate the deployment of autonomous systems in complex domains requiring deep contextual understanding, such as scientific discovery or financial analysis.
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Read at arXiv cs.AI