Multi-view Outlier Detection for Attributed Network Based on Knowledge Fusion

Outlier detection on attributed networks is of important theoretical and practical significance in the network security, ecommerce, financial transaction and many other fields, and receives more and more attentions in recent years. Most existing outlier detection methods usually generate decisions by pattern mining on the network structure or node attributes. However, it is difficult to make a reliable description for abnormal objects by just relying on the limited attribute and structure information directly available from given network data. Furthermore, the nodes in networks are usually associated with abundant domain knowledge in the real world, which has great potential value for outlier detection. To this end, this paper proposes a multi-view network outlier detection model based on knowledge fusion, which identifies the abnormal pattern effectively by complementary fusion of network data and associated knowledge under the multi-view learning mode. Firstly, the model applies TransR to extract knowledge vector representation from domain knowledge graph, and constructs a twin network with the topology structure of the input network. Then, the attribute encoder and the knowledge encoder are constructed under the multi-view learning framework to embed he attributed network and its twin network into their respective representation spaces separately. On this basis, the network embeddings in two views are integrated into a unified representation by the aggregator. Finally, the abnormal score of each node is evaluated by integrating the reconstruction errors in the two different dimensions, and the abnormal nodes in the network are then recognized. Extensive experiments on real network datasets demonstrate that the proposed model can realize effective fusion of domain knowledge and acquire better outlier detection performance than baseline approaches. © 2023 Science Press. All rights reserved.