Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Mining social networks for anomalies: Methods and challenges(Academic Press, 2016) Bindu, P.V.; Santhi Thilagam, P.S.Online social networks have received a dramatic increase of interest in the last decade due to the growth of Internet and Web 2.0. They are among the most popular sites on the Internet that are being used in almost all areas of life including education, medical, entertainment, business, and telemarketing. Unfortunately, they have become primary targets for malicious users who attempt to perform illegal activities and cause harm to other users. The unusual behavior of such users can be identified by using anomaly detection techniques. Anomaly detection in social networks refers to the problem of identifying the strange and unexpected behavior of users by exploring the patterns hidden in the networks, as the patterns of interaction of such users deviate significantly from the normal users of the networks. Even though a multitude of anomaly detection methods have been developed for different problem settings, this field is still relatively young and rapidly growing. Hence, there is a growing need for an organized study of the work done in the area of anomaly detection in social networks. In this paper, we provide a comprehensive review of a large set of methods for mining social networks for anomalies by providing a multi-level taxonomy to categorize the existing techniques based on the nature of input network, the type of anomalies they detect, and the underlying anomaly detection approach. In addition, this paper highlights the various application scenarios where these methods have been used, and explores the research challenges and open issues in this field. © 2016 Elsevier Ltd. All rights reserved.Item Discovering suspicious behavior in multilayer social networks(Elsevier Ltd, 2017) Bindu, P.V.; Santhi Thilagam, P.S.; Ahuja, D.Discovering suspicious and illicit behavior in social networks is a significant problem in social network analysis. The patterns of interactions of suspicious users are quite different from their peers and can be identified by using anomaly detection techniques. The existing anomaly detection techniques on social networks focus on networks with only one type of interaction among the users. However, human interactions are inherently multiplex in nature with multiple types of relationships existing among the users, leading to the formation of multilayer social networks. In this paper, we investigate the problem of anomaly detection on multilayer social networks by combining the rich information available in multiple network layers. We propose a pioneer approach namely ADOMS (Anomaly Detection On Multilayer Social networks), an unsupervised, parameter-free, and network feature-based methodology, that automatically detects anomalous users in a multilayer social network and rank them according to their anomalousness. We consider the two well-known anomalous patterns of clique/near-clique and star/near-star anomalies in social networks, and users are ranked according to the degree of similarity of their neighborhoods in different layers to stars or cliques. Experimental results on several real-world multilayer network datasets demonstrate that our approach can effectively detect anomalous nodes in multilayer social networks. © 2017 Elsevier Ltd