Performance Testing of RNSC and MCL Algorithms on Random Geometric Graphs
The exploration of quality clusters in complex networks is an important issue in many disciplines, which still remains a challenging task. Many graph clustering algorithms came into the field in the recent past but they were not giving satisfactory performance on the basis of robustness, optimality, etc. So, it is most difficult task to decide which one is giving more beneficial clustering results compared to others in case of real-world problems. In this paper, performance of RNSC (Restricted Neighborhood Search Clustering) and MCL (Markov CLustering) algorithms are evaluated on a Random Geometric Graph (RGG). RNSC uses stochastic local search method for clustering of a graph.