  Anomaly detection on dynamic graphs refers to detecting entities whose
behaviors obviously deviate from the norms observed within graphs and their
temporal information. This field has drawn increasing attention due to its
application in finance, network security, social networks, and more. However,
existing methods face two challenges: dynamic structure constructing challenge
- difficulties in capturing graph structure with complex time information and
negative sampling challenge - unable to construct excellent negative samples
for unsupervised learning. To address these challenges, we propose Unsupervised
Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first
challenge, we propose a continuous dynamic graph model to capture the
fine-grained information, which breaks the limit of existing discrete methods.
Specifically, we employ a message-passing framework combined with positional
features to get edge embeddings, which are decoded to identify anomalies. For
the second challenge, we pioneer the use of Generative Adversarial Networks to
generate negative interactions. Moreover, we design a loss function to alter
the training goal of the generator while ensuring the diversity and quality of
generated samples. Extensive experiments demonstrate that our proposed GADY
significantly outperforms the previous state-of-the-art method on three
real-world datasets. Supplementary experiments further validate the
effectiveness of our model design and the necessity of each module.
