A Density-Based Approach to Detect Community Evolutionary Events in Online Social Networks

With the advent of Web 2.0/3.0 supported social media, Online Social Networks (OSNs) have emerged as one of the popular communication tools to interact with similar interest groups around the globe. Due to increasing popularity of OSNs and exponential growth in the number of their users, a significant amount of research efforts has been diverted towards analyzing user-generated data available on these networks, and as a result various community mining techniques have been proposed by different research groups. But, most of the existing techniques consider the number of OSN users as a fixed set, which is not always true in a real scenario, rather the OSNs are dynamic in the sense that many users join/leave the network on a regular basis. Considering such dynamism, this chapter presents a density-based community mining method, OCTracker, for tracking overlapping community evolution in online social networks. The proposed approach adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures and automatically detects evolutionary events including birth, growth, contraction, merge, split, and death of communities with time. Unlike other density-based community detection methods, the proposed method does not require the neighborhood threshold parameter to be set by the users, rather it automatically determines the same for each node locally. Evaluation results on various datasets reveal that the proposed method is computationally efficient and naturally scales to large social networks.