  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).
