197 citations · 339 across the 28 of their papers we have counts for
Showing 2023 · cs.LGShow all
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cs.LG2023
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Qiaoyu Tan +4
Unsupervised graph anomaly detection is crucial for various practical applications as it aims to identify anomalies in a graph that exhibit rare patterns deviating significantly fr…
cs.LG2023★ 1 cited
Homophily-enhanced Structure Learning for Graph Clustering
Ming Gu, Gaoming Yang, Sheng Zhou +5
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existi…
cs.LG2023★ 1 cited
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Yuanchen Bei, Hao Xu, Sheng Zhou +5
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the adva…