96 citations · 229 across the 49 of their papers we have counts for
7 papers · 1 filter
GRAND-HC: Graph-Refined Author Name Disambiguation
Yuanhao Sun, Zhouyang Jin, Yi Xu +5
From-Scratch Name Disambiguation (SND) groups papers sharing an ambiguous name into clusters of distinct real-world authors. Existing methods suffer from two critical limitations:…
Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent
Yiming Liu, Bin Lu, Meng Jin +6
Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and sparse. While vast historical r…
SLASH the Sink: Sharpening Structural Attention Inside LLMs
Yiming Liu, Bin Lu, Xinbing Wang +2
Large Language Models (LLMs) show remarkable semantic understanding but often struggle with structural understanding when processing graph topologies in a serialized format. Existi…
Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
Xu Bai, Bin Lu, Kun Zhang +4
Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic prop…
Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities
Ze Zhao, Yuhui He, Lyuwen Wu +6
Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at capturing relational dynamics,…
<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…
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…