Comparative Performance Analysis Of Rnsc And Mcl Algorithms On Power-Law Distribution

Abstract

Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A. D. King only for undirected and unweighted random graphs. Another popular graph clustering algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e. nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of human sexual contacts, or the chemical network of a cell etc., are basically following power-law distribution to represent different real-life systems. This paper uses real large-scale power-law distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law distribution datasets reveal the effectiveness of our approach to comparative performance measure of these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and normalized mutual information (NMI).