15 citations · 29 across the 10 of their papers we have counts for
4 papers · 1 filter
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models
Jiarui Ji, Runlin Lei, Jialing Bi +6
The structural properties of naturally arising social graphs are extensively studied to understand their evolution. Prior approaches for modeling network dynamics typically rely on…
Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents
Yuwei Hu, Runlin Lei, Xinyi Huang +2
Recent research has explored the use of Large Language Models (LLMs) for tackling complex graph reasoning tasks. However, due to the intricacies of graph structures and the inheren…
Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks
Jie Peng, Runlin Lei, Zhewei Wei
The drastic performance degradation of Graph Neural Networks (GNNs) as the depth of the graph propagation layers exceeds 8-10 is widely attributed to a phenomenon of Over-smoothing…
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
Runlin Lei, Yuwei Hu, Yuchen Ren +1
Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nod…