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 Neighbourhood 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. RNSC algorithm tries to achieve optimal cost clustering by assigning some cost functions to the set of clusterings of a graph. Another standard clustering algorithm MCL is based on stochastic flow simulation model. RGG has conventionally been associated with areas such as statistical physics and hypothesis testing but have achieved new relevance with the advent of wireless ad-hoc and sensor networks. In this study, the performance testing of these methods is conducted on the basis of cost of clustering, cluster size, modularity index of clustering results and normalized mutual information (NMI) using both real and synthetic RGG. General Terms General Terms: Graph clustering, Data mining et. al.