ConsE: Consistency Exploitation for Semi-Supervised Anomaly Detection in Graphs
2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN(2023)
摘要
Graph anomaly detection has attracted considerable interest due to the wide use of graph structure data. Several GNN-based anomaly detection methods discover anomalies through the powerful node representation ability of GNNs. However, real-world graphs are typically rarely labeled, which leads these deep learning methods to face the challenge of under-fitting. A fundamental question here is: can anomalies in graphs be detected with few annotations? In this paper, we propose a novel semi-supervised anomaly detection method in graphs based on Consistency Exploitation (ConsE). First, ConsE adopts a consistency-based neighbor sampler, which ensures the consistency of a central node and its neighbors on attributes and categories during the aggregation process through attribute similarity and soft pseudo-labels. Afterward, ConsE encourages the consistency of node representations generated by a dedicated neighbor sampler and a generic neighbor sampler to improve its robustness in complex neighborhoods. Experimental results on real-world datasets demonstrate that our model significantly outperforms several state-of-the-art baseline methods.
更多查看译文
关键词
Anomaly Detection,Semi-supervised Learning,Consistency Regularization
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要