Discovering Strong Communities in Social Networks Through Chromatic Correlation Clustering

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.