25 citations · 73 across the 36 of their papers we have counts for
4 papers · 1 filter
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Yizhen Zheng +1
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e…
Towards Self-Interpretable Graph-Level Anomaly Detection
Yixin Liu, Kaize Ding, Qinghua Lu +3
Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. However, current works primarily focus on…
Integrating Graphs with Large Language Models: Methods and Prospects
Shirui Pan, Yizhen Zheng, Yixin Liu
Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and…
Towards Data-centric Graph Machine Learning: Review and Outlook
Xin Zheng, Yixin Liu, Zhifeng Bao +4
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent yea…