25 citations · 73 across the 36 of their papers we have counts for
12 papers · 1 filter
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Rui Miao +5
Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks. As MAS become increasingly autonomous in various safety-critical…
Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
Junjun Pan, Yixin Liu, Chuan Zhou +3
Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume ide…
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
Junjun Pan, Yu Zheng, Yue Tan +1
Graph anomaly detection (GAD) has attracted increasing attention in recent years for identifying malicious samples in a wide range of graph-based applications, such as social media…
Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects
Yixin Liu, Guibin Zhang, Kun Wang +2
Autonomous agents based on large language models (LLMs) have demonstrated impressive capabilities in a wide range of applications, including web navigation, software development, a…
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Yunfeng Zhao, Yixin Liu, Shiyuan Li +3
Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. D…
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks
Rui Miao, Yixin Liu, Yili Wang +5
The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through int…
Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation
Shiyuan Li, Yixin Liu, Qingsong Wen +2
Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains. The effectiveness of M…
Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
Qingfeng Chen, Shiyuan Li, Yixin Liu +3
Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertai…
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems
Xu Shen, Yixin Liu, Yiwei Dai +5
The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and ef…
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
Junjun Pan, Yixin Liu, Xin Zheng +4
Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic conn…
Out-of-Distribution Detection on Graphs: A Survey
Tingyi Cai, Yunliang Jiang, Yixin Liu +3
Graph machine learning has witnessed rapid growth, driving advancements across diverse domains. However, the in-distribution assumption, where training and testing data share the s…
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling
Xu Shen, Yixin Liu, Yili Wang +5
Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph learning, as real-world graph data often exhibit diverse and shifting environments that tradit…