Abstract
Complex relationships within the data are modeled as information network in various application areas of data mining. Identification of connected groups of nodes associated with similar information is one of the common interests behind such modeling. These connected groups are referred as community structure. This work investigates community structure by incorporating a sociological property known as ego network. Ego network facilitates personalized view of relationships. We introduce the notion of mutual interest in the relationship by utilizing such personalized view and re-define community structure. Different from classical way of defining communities through dense connectivity, proposed definition incorporates two properties: Reachability and Isolability. Reachability measures the ability of any node to reach out members of community, while Isolability accounts the ability of any community to isolate itself from rest of the network. Exploiting new definition of community structure, we propose an algorithm for identifying communities. Experimental results on a variety of real world data and synthetic data show communities identified with proposed algorithm is highly inclined towards accuracy in comparison to other state-of-the-art approaches.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call