TY - JOUR
T1 - Generic anomalous vertices detection utilizing a link prediction algorithm
AU - Kagan, Dima
AU - Elovichi, Yuval
AU - Fire, Michael
N1 - Funding Information: Acknowledgements We would like to thank Carol Teegarden and Robin Levy-Stevenson for editing and proofreading this article to completion. We also thank the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery, and the Moore/Sloan Data Science Environment Project at the University of Washington for supporting this study. Finally, we would like to thank the anonymous reviewers for their helpful comments. Funding Information: We would like to thank Carol Teegarden and Robin Levy-Stevenson for editing and proofreading this article to completion. We also thank the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery, and the Moore/Sloan Data Science Environment Project at the University of Washington for supporting this study. Finally, we would like to thank the anonymous reviewers for their helpful comments. Publisher Copyright: © 2018, Springer-Verlag GmbH Austria, part of Springer Nature.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - In the past decade, graph-based structures have penetrated nearly every aspect of our lives. The detection of anomalies in these networks has become increasingly important, such as in exposing infected endpoints in computer networks or identifying socialbots. In this study, we present a novel unsupervised two-layered meta-classifier that can detect irregular vertices in complex networks solely by utilizing topology-based features. Following the reasoning that a vertex with many improbable links has a higher likelihood of being anomalous, we applied our method on 10 networks of various scales, from a network of several dozen students to online networks with millions of vertices. In every scenario, we succeeded in identifying anomalous vertices with lower false positive rates and higher AUCs compared to other prevalent methods. Moreover, we demonstrated that the presented algorithm is generic, and efficient both in revealing fake users and in disclosing the influential people in social networks.
AB - In the past decade, graph-based structures have penetrated nearly every aspect of our lives. The detection of anomalies in these networks has become increasingly important, such as in exposing infected endpoints in computer networks or identifying socialbots. In this study, we present a novel unsupervised two-layered meta-classifier that can detect irregular vertices in complex networks solely by utilizing topology-based features. Following the reasoning that a vertex with many improbable links has a higher likelihood of being anomalous, we applied our method on 10 networks of various scales, from a network of several dozen students to online networks with millions of vertices. In every scenario, we succeeded in identifying anomalous vertices with lower false positive rates and higher AUCs compared to other prevalent methods. Moreover, we demonstrated that the presented algorithm is generic, and efficient both in revealing fake users and in disclosing the influential people in social networks.
UR - http://www.scopus.com/inward/record.url?scp=85045074928&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s13278-018-0503-4
DO - https://doi.org/10.1007/s13278-018-0503-4
M3 - Article
SN - 1869-5450
VL - 8
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 27
ER -