Generative Semi-supervised Graph Anomaly Detection
CoRR(2024)
摘要
This work considers a practical semi-supervised graph anomaly detection (GAD)
scenario, where part of the nodes in a graph are known to be normal,
contrasting to the unsupervised setting in most GAD studies with a fully
unlabeled graph. As expected, we find that having access to these normal nodes
helps enhance the detection performance of existing unsupervised GAD methods
when they are adapted to the semi-supervised setting. However, their
utilization of these normal nodes is limited. In this paper, we propose a novel
Generative GAD approach (GGAD) for the semi-supervised scenario to better
exploit the normal nodes. The key idea is to generate outlier nodes that
assimilate anomaly nodes in both local structure and node representations for
providing effective negative node samples in training a discriminative
one-class classifier. There have been many generative anomaly detection
approaches, but they are designed for non-graph data, and as a result, they
fail to take account of the graph structure information. Our approach tackles
this problem by generating graph structure-aware outlier nodes that have
asymmetric affinity separability from normal nodes while being enforced to
achieve egocentric closeness to normal nodes in the node representation space.
Comprehensive experiments on four real-world datasets are performed to
establish a benchmark for semi-supervised GAD and show that GGAD substantially
outperforms state-of-the-art unsupervised and semi-supervised GAD methods with
varying numbers of training normal nodes. Code will be made available at
https://github.com/mala-lab/GGAD.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要