An Efficient behavioural analysis of Graph Clustering Algorithms via Random Graphs

proposed last research entitled "An Effective Data Comparison of Graph Clustering Algorithms via Random Graphs" compared two mostly used algorithms for graph clustering i.e. restricted neighborhood search and markov clustering algorithms via random graph generators i.e. Erdos- Renyi and power law graphs. This paper is an extension to our last research work. In this we have examined an efficient behavioral analysis of both algorithms via random graphs. This paper mainly shows the behavior of both the algorithms under certain parameters which we have used. Previously in case of Erdos-renyii we used graphs with 1000 nodes with variable edge densities, while in this paper we have modified the number of nodes from 1000 to 15000 with variable edge densities ranging from 0.1 to 0.5 while in case of Power-law we have variable number of nodes ranging from 1000 to 15000. This paper also depicts as to which algorithm works more efficient, whether in case of Erdos-Renyi or Power-Law graphs. Our last research showed that in case of Erdos-Renyi graph run time of RNSC algorithm is better as compared to MCL for graph having nodes less than 2000 but as nodes keep on increasing the run time of RNSC increases drastically while run time of MCL doesn't increase, so MCL is better in case of Erdos-Renyi graph having more than 2000 nodes and having high connectivity between the nodes (12) while in this paper it is clearly visible that both RNSC and MCL works better in case of Power-Law graph as compared to Erdos- Renyi graph which clearly states that both algorithm shows some similar characteristics in graphs where edge connectedness is not very high for all vertices. Furthermore we studied the behaviour of RNSC in case of Erdos-Renyi individually also.