AANE: Anomaly Aware Network Embedding for Anomalous Link Detection

Existing network embedding models regard all the links in a network as normal and model them without distinction. In real networks, there may be anomalous links like noise or adversarial links. We explicitly consider the existence of anomalous links in a network and propose anomaly aware network embedding (AANE) model. The key of AANE is the design of a new loss, which consists of anomaly aware loss and adjusted fitting loss. We adopt an anomaly indicator to iteratively select significant anomalous links from the network during model training, and removal loss and deviation loss are designed to model the reconstruction errors of selected anomalous and normal links respectively. To instantiate AANE, AAGAE and AAGCN are implemented on graph auto-encoder (GAE) and graph convolution based auto-encoder (GCNAE) respectively. For the purpose of evaluation, a heuristic anomalous link generation algorithm is proposed and by using the algorithm we generate anomalous links into six real world network datasets. Experimental results show that AANE outperforms both basic and competitive network embedding models in terms of anomalous link detection performance in most cases.