Discovering Strong Communities in Social Networks Through Chromatic Correlation Clustering

Title:Discovering Strong Communities in Social Networks Through Chromatic Correlation Clustering

Volume: 14 Issue: 5

Author(s): Jaishri Gothania*Shashi K. Rathore

Affiliation:

• Department of Computer Science & Engineering, School of Engineering & Technology, Career Point University, Kota, Rajasthan,India

Keywords: chromatic balls, correlation clustering, community detection, community discovery, strong communities, social networks.

Abstract:

Background: Complex systems involved in biochemistry, neuroscience, physics, engineering and social science are primarily studied and modeled through network structures. The connectivity patterns within these interaction networks are discovered through clustering-like techniques. Community discovery is a related problem to find patterns in networks.

Objectives: Existing algorithms either try to find few large communities in networks; or try to partition network into small strongly connected communities; that too is time consuming and parameterdependant.

Methods/Results: This paper proposes a chromatic correlation clustering method to discover small strong communities in an interaction network in heuristic manner to have low time complexity and a parameter free method. Comparison with other methods over synthetic data is done.

Conclusion: Interaction networks are very large, sparse containing few small dense communities that can be discovered only through method specifically designed for the purpose.

Cite this article as:

Gothania Jaishri *, Rathore K. Shashi, Discovering Strong Communities in Social Networks Through Chromatic Correlation Clustering, Recent Advances in Computer Science and Communications 2021; 14 (5) . https://dx.doi.org/10.2174/2666255813666190923101910