An anomaly aware network embedding framework for unsupervised anomalous link detection

Most existing network embedding based anomalous link detection methods regard network embedding and anomalous link detection as two independent tasks. However, removing anomalous links from the original network can reduce the data noise, thus hopefully improving the performance of network embedding models and anomalous link detection. In this paper, we propose an Anomaly Aware Network Embedding (AANE) framework by simultaneously learning node embedding and detecting anomalous links in a unified way. To instantiate the AANE framework, we propose a heuristic anomalous link selection based model AANE-H and an embedding disentangling based model AANE-D on Graph Auto-Encoder (GAE). In AANE-H, we adopt an anomalous link selector to iteratively select significant anomalous links based on a heuristic rule during model training, while in AANE-D the normal and anomalous links are generated by disentangled normal and anomalous embedding respectively. For the evaluation purpose, we propose a heuristic anomalous link generation algorithm to inject synthetic anomalous links into six real-world network datasets used in our experiments. Experimental results show that AANE outperforms both the state-of-the-art network embedding models and anomalous node detection models in terms of anomalous link detection performance. As a general network embedding model, AANE can also improve other downstream tasks like node classification.