3 papers
cs.LG2024
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Jinyu Cai, Yunhe Zhang, Zhoumin Lu +2
Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and comple…
cs.LG2023
Self-Discriminative Modeling for Anomalous Graph Detection
Jinyu Cai, Yunhe Zhang, Jicong Fan
This paper studies the problem of detecting anomalous graphs using a machine learning model trained on only normal graphs, which has many applications in molecule, biology, and soc…
cs.LG2023
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
Jinyu Cai, Yi Han, Wenzhong Guo +1
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are…