2 papers
cs.AI2026
Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression
Zijun Di, Bin Lu, Huquan Kang +5
Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding. Recent studies typically focus on verbalizing the graph structur…
cs.CL2026
<SOG_k>: One LLM Token for Explicit Graph Structural Understanding
Jingyao Wu, Bin Lu, Zijun Di +5
Large language models show great potential in unstructured data understanding, but still face significant challenges with graphs due to their structural hallucination. Existing app…