TY - GEN
T1 - Spotting terrorists by learning behavior-aware heterogeneous network embedding
AU - Wang, Pei Chi
AU - Li, Cheng Te
N1 - Funding Information:
This work was supported by Ministry of Science and Technology (MOST) Taiwan with grants 108-2636-E-006-002 (MOST Young Scholar Fellowship Program) and 108-2218-E-006-036, and supported by Academia Sinica Thematic Research Program with grant AS-107-TP-M05.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/3
Y1 - 2019/11/3
N2 - 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.
AB - 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.
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U2 - 10.1145/3357384.3358078
DO - 10.1145/3357384.3358078
M3 - Conference contribution
AN - SCOPUS:85075435800
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2097
EP - 2100
BT - CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 28th ACM International Conference on Information and Knowledge Management, CIKM 2019
Y2 - 3 November 2019 through 7 November 2019
ER -
