A Study of Identifying Structural Hole Spanners in Large Scale Networs

The expansion of internet and increasingly evolving Social Network Sites (SNS) has facilitated internet-users to access a common stage for communication. With the increase in communications between a group of internet-users or between a pair of individuals, keeping track of their interactions is becoming a complex task. Social Networks datasets contain valuable information that can be examined to extract useful patterns. Social Network Analysis (SNA) is a study of patterns and relations in data. This involves identifying Communities or sub-communities from a large network of nodes. The individuals or entities in such Networks are partitioned into closely connected groups called Communities. There can be network structures that connect two or more communities. The Individuals that connect communities in such networks are termed as Structural Hole Spanners (SHS). Closely related communities and Structural Hole Spanners play an important role in various business applications. Therefore the study of identifying the dense communities and highly qualified structural holes in a large network of nodes has become study of interest among researchers. The evolution of Big Data Analytics is making it feasible. This paper studies the techniques of community detection and structural holes identification in literature. It made a preliminary study on SHS and presented two popular Structural Holes Identification Methods with their results.