PROJECT TITLE :

HOCTracker: Tracking the Evolution of Hierarchical and Overlapping Communities in Dynamic Social Networks

ABSTRACT:

In this paper, we propose a unified framework, HOCTracker, for tracking the evolution of hierarchical and overlapping communities in online social networks. Unlike most of the dynamic community detection methods, HOCTracker adapts a preliminary community structure towards dynamic changes in social networks using a novel density-primarily based approach for detecting overlapping community structures, and automatically tracks evolutionary events like birth, growth, contraction, merge, split, and death of communities. It uses a unique and economical log-based mostly approach to map evolutionary relations between communities identified at 2 consecutive time-steps of a dynamic network, which significantly reduces the quantity of community comparisons. Moreover, it does not need an ageing operate to remove recent interactions for identifying community evolutionary events. HOCTracker is applicable to diirected/undirected and weighted/unweighted networks. Experimental results have shown that community structures identified by HOCTracker on some well-known benchmark networks are vital and normally higher that the community structures identified by the state-of-the-art ways.