10 citations · 17 across the 10 of their papers we have counts for
4 papers · 1 filter
Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
Runlin Lei, Lu Yi, Mingguo He +4
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a comprehensive understanding of their ro…
Rethinking and Benchmarking Large Language Models for Graph Reasoning
Yuwei Hu, Xinyi Huang, Zhewei Wei +2
Large Language Models (LLMs) for Graph Reasoning have been extensively studied over the past two years, involving enabling LLMs to understand graph structures and reason on graphs…
GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
Jie Peng, Jiarui Ji, Runlin Lei +3
Dynamic Text-Attributed Graphs (DyTAGs), which intricately integrate structural, temporal, and textual attributes, are crucial for modeling complex real-world systems. However, mos…
Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs
Runlin Lei, Jiarui Ji, Haipeng Ding +4
With the rise of large language models (LLMs), there has been growing interest in Graph Foundation Models (GFMs) for graph-based tasks. By leveraging LLMs as predictors, GFMs have…