Outlier detection in social networks leveraging community structure

Social networks have become an important aspect of our modern times and are gradually becoming an integral means of communication worldwide. Overwhelming amounts of data are being transferred over social networks every day. Hence ensuring security becomes a necessity. Suspicious users or spammers may pose a threat to the information and data shared by users over the network. With this in mind, outliers detection is a crucial aspect of network communication. In our paper, a new technique is proposed to identify the anomalies in a network from a global perspective by using the network community structure. In general, state-of-the-art outliers detection algorithms mainly focus on the individual nodes and their direct neighborhood. But our technique considers only those nodes which tend to belong to multiple communities or whose neighbors belong to the same community or do not belong to any community. Results after experiments on synthetic and real-world networks show an improvement of 7-10% and 29% in F-Score and Jaccard similarity, respectively, compared to the state-of-the-art algorithms. Furthermore, we achieve almost 1.83 times speedup compared to the state-of-the-art algorithms.