User Interface for Community Detection in Social Networks

The ongoing issue in social network is detecting the communities for large data sets efficiently, stabilizing the communities in the network so that we get same structure over different runs for same network data set pose a challenging problem in the research community. Although, there were various attempts in the past to come out with an efficient and cost effective algorithm that can perform an efficient computation for community detection, yet very few of them are found to be reliable or applicable in real-time applications. Label Rank is one such algorithm that is recently proposed which provide insights to several other community detection algorithms due to its efficiency in detecting and stabilizing the communities for given network data set. In this work, a user interface has been developed which performs community detection using Label Rank algorithm for different data sets and real-world data set. The analysis results were obtained over different executions for different network data sets.