Comparative Analysis of Structural Characteristics of Social Networks and Their Relevance in Community Detection

Social Network Analysis based on structural properties is relevant to many application domains including influence maximization, product recommendation, and viral marketing to name a few. The study of structural properties has a vital role in effective community detection and researchers have suggested several algorithms in this direction. The research paper not only gives details of various structural characteristics and their relevance for community detection but it also covers community visualization tools and their useful features. Along with the mapping of structural properties and community detection, efforts have also been made towards understanding the essential community evaluation metrics to know the goodness of communities resulting after applying various algorithms. Network characteristics vary for disjoint and overlapping community detection, so, social network datasets are analyzed to produce different network statistics for a better understanding of their suitability for disjoint and overlapping community detection. The research paper also covers applications of community detection to motivate the researchers for exploring the possibility of using characteristics of social networks for detecting communities for reducing computation time.