Application of Clustering to Analyze Academic Social Networks

Abstract

Social network is a group of individuals with diverse social interactions amongst them. The network is of large scale and distributed due to involvement of more people from different parts of the globe. Quantitative analysis of networks is need of the hour due to its' rippling influence on the network dynamics and in turn the society. Clustering helps us to group people with similar characteristics to analyze the dense social networks. We have considered similarity measures for statistical analysis of social network. When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis. We have chosen academic social networks and clustered nodes to simplify network analysis. The ontology of research interests is considered to measure similarity between unstructured data elements extracted from profile pages of members of an academic social network.