Faculty Publications
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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 LtdItem Discovering spammer communities in twitter(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Bindu, P.V.; Mishra, R.; Santhi Thilagam, P.S.Online social networks have become immensely popular in recent years and have become the major sources for tracking the reverberation of events and news throughout the world. However, the diversity and popularity of online social networks attract malicious users to inject new forms of spam. Spamming is a malicious activity where a fake user spreads unsolicited messages in the form of bulk message, fraudulent review, malware/virus, hate speech, profanity, or advertising for marketing scam. In addition, it is found that spammers usually form a connected community of spam accounts and use them to spread spam to a large set of legitimate users. Consequently, it is highly desirable to detect such spammer communities existing in social networks. Even though a significant amount of work has been done in the field of detecting spam messages and accounts, not much research has been done in detecting spammer communities and hidden spam accounts. In this work, an unsupervised approach called SpamCom is proposed for detecting spammer communities in Twitter. We model the Twitter network as a multilayer social network and exploit the existence of overlapping community-based features of users represented in the form of Hypergraphs to identify spammers based on their structural behavior and URL characteristics. The use of community-based features, graph and URL characteristics of user accounts, and content similarity among users make our technique very robust and efficient. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.Item Influence maximisation in social networks(Inderscience Publishers, 2019) Tejaswi, V.; Bindu, P.V.; Santhi Thilagam, P.S.Influence maximisation is one of the significant research areas in social network analysis. It helps in identifying influential entities from social networks that can be used in marketing, election campaigns, outbreak detection and so on. Influence maximisation deals with the problem of finding a subset of nodes called seeds in the social network such that these nodes will eventually spread maximum influence in the network. This is an NP-hard problem. The aim of this paper is to provide a complete understanding of the influence maximisation problem. This paper focuses on providing an overview on the influence maximisation problem, and covers three major aspects: 1) different types of inputs required; 2) influence propagation models that map the spread of influence in the network; 3) the approximation algorithms proposed for seed set selection. In addition, we provide the state of the art and describe the open problems in this domain. © 2019 Inderscience Enterprises Ltd.Item Hindi fake news detection using transformer ensembles(Elsevier Ltd, 2023) Praseed, A.; Rodrigues, J.; Santhi Thilagam, P.S.In the past few decades, due to the growth of social networking sites such as Whatsapp and Facebook, information distribution has been at a level never seen before. Knowing the integrity of information has been a long-standing problem, even more so for the regional languages. Regional languages, such as Hindi, raise challenging problems for fake news detection as they tend to be resource constrained. This limits the amount of data available to efficiently train models for these languages. Most of the existing techniques to detect fake news is targeted towards the English language or involves the manual translation of the language to the English language and then proceeding with Deep Learning methods. Pre-trained transformer based models such as BERT are fine-tuned for the task of fake news detection and are commonly employed for detecting fake news. Other pre-trained transformer models, such as ELECTRA and RoBERTa have also been shown to be able to detect fake news in multiple languages after suitable fine-tuning. In this work, we propose a method for detecting fake news in resource constrained languages such as Hindi more efficiently by using an ensemble of pre-trained transformer models, all of which are individually fine-tuned for the task of fake news detection. We demonstrate that the use of such a transformer ensemble consisting of XLM-RoBERTa, mBERT and ELECTRA is able to improve the efficiency of fake news detection in Hindi by overcoming the drawbacks of individual transformer models. © 2022 Elsevier Ltd