Spotting terrorists by learning behavior-aware heterogeneous network embedding
PC Wang, CT Li - Proceedings of the 28th ACM international conference …, 2019 - dl.acm.org
PC Wang, CT Li
Proceedings of the 28th ACM international conference on information and …, 2019•dl.acm.orgHeterogeneous network is a useful data representation in depicting complex interactions
among multi-typed entities and relations. In this work, by representing criminal and terrorism
activities as a heterogeneous network, we propose a novel unsupervised method, Outlier
Spotting with behavior-aware Network Embedding (OSNE), to identify terrorists among
potential criminals. The basic idea of OSNE is to exploit high-order relation paths for
translation-based embedding learning, and distinguish same-type entities based on …
among multi-typed entities and relations. In this work, by representing criminal and terrorism
activities as a heterogeneous network, we propose a novel unsupervised method, Outlier
Spotting with behavior-aware Network Embedding (OSNE), to identify terrorists among
potential criminals. The basic idea of OSNE is to exploit high-order relation paths for
translation-based embedding learning, and distinguish same-type entities based on …
Heterogeneous network is a useful data representation in depicting complex interactions among multi-typed entities and relations. In this work, by representing criminal and terrorism activities as a heterogeneous network, we propose a novel unsupervised method, Outlier Spotting with behavior-aware Network Embedding (OSNE), to identify terrorists among potential criminals. The basic idea of OSNE is to exploit high-order relation paths for translation-based embedding learning, and distinguish same-type entities based on behavior penalty and type-aware negative sampling. We evaluate the effectiveness of OSNE using six criminal network datasets provided by DARPA, and make comparison with strong competitors. The results exhibit the promising performance of OSNE.