GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
This addresses the problem of anomaly detection in graph data for applications like network security or social media analysis, representing a novel method for a known bottleneck.
The paper tackles the problem of detecting anomalous snapshots in attributed graphs by proposing GraphAnoGAN, a framework that uses generative and discriminative models to rank anomalies, achieving 28.29% higher precision and 22.01% higher recall on average compared to the best baseline across four real-world datasets.
Finding anomalous snapshots from a graph has garnered huge attention recently. Existing studies address the problem using shallow learning mechanisms such as subspace selection, ego-network, or community analysis. These models do not take into account the multifaceted interactions between the structure and attributes in the network. In this paper, we propose GraphAnoGAN, an anomalous snapshot ranking framework, which consists of two core components -- generative and discriminative models. Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not. Experiments on 4 real-world networks show that GraphAnoGAN outperforms 6 baselines with a significant margin (28.29% and 22.01% higher precision and recall, respectively compared to the best baseline, averaged across all datasets).