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1 Social networks have highly been used to understand the behavior and activities of individuals in nature and society. They are being used as a means to communicate, diffuse information, and to control the spread of diseases and computer viruses, in addition to many other tasks. Business organizations look upon social networks as an opportunity to spread the word-of-mouth for viral marketing and this task has gained significance with the popularity of Online Social Networks (OSNs). However, an important characteristic of social networks, including OSNs, which is the existence of overlapping communities of users, has not been exploited yet for the task of viral marketing even though it seems promising. This paper aims to present the importance of identifying overlapping communities for the task of viral marketing in social networks and also provides some experimental results on an email network to back the claims.

Abstract.As the online social network technology is gaining all time high popularity and usage, the malicious behavior and attacks of spammers are getting smarter and difficult to track. The newer spamming approaches using the social engineering concepts are making traditional spam and spammer detection techniques obsolete. Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult. Newer approaches for spammer detection using topological features are gaining attention. Further, the evaluation of ensemble classifiers for detection of spammers over social networking behavior-based features is still in its infancy. In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features of legitimate users and spammers in online social networks. The proposed method aims to identify topological and community-based features from users' interaction network and uses popular classifier ensembles -bagging and boosting to identify spammers in online social networks. Experimental evaluation of the proposed method is done over a real-world data set with artificial spammers that follow a behavior as reported in earlier literature. The experimental results reveal that the identified features are highly discriminative to identify spammers in online social networks.