Network Based Community Detection By Using Bisecting Hierarchical Clustering

Social media is the platform where human interacts with each other, it is becoming part of our life. One of the task of detecting communities in social networks are real time networks that can be classified by using clustering techniques to extract and find hidden communities from the network. For example Facebook, the different communities can be extracted like people belonging to different interests and communities and this information can be used for marketing purposes. Nowadays, studies involving this clustering processes are basically composed of modularity maximization. In this paper the author proposes a bisecting hierarchical clustering based on a measure known as inter and inter cluster similarities to detect communities within the networks. The experiments indicates a successful performance.