Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

Abstract

Community structure is one of the main structural features of networks and detecting overlapped community structure is an important field in social network analysis. In recent years, local community detection algorithms which detect overlapped community structure have been developed. However, the most existing algorithms suffer unstable community structure because the influences of parameter for controlling community's resolution of fitness evaluation functions where used in identifying communities. Therefore, this article designs the optimized parameter evaluation formula to avoid the parameter influences and the algorithm is modelled on local expansion strategy. In this work, firstly identifies the seed or core node by using extended jaccard similarity and form initial community via seed. Then local community is detected by expanding the initial community with fitness function based on proposed optimized parameter evaluation and finally overlapped nodes are identified by merging detected local communities. In this article, the algorithm is implemented by using small dataset from network data repository site and large networks from Stanford large network datasets collection. The performance results of algorithm are compared with LFM (Local Fitness Method), OSLOM (Order Statistics Local Optimization Method), DEMON (Democratic Estimate of Modular Organization of a Network), NILPA (Node Important based Label Propagation) and GREESE (Greedy Coupled-seeds Expansion) algorithm. Then, the proposed algorithm proves that it effectively performs and saves execution time.