Advanced Cost Based Graph Clustering Algorithm for Random Geometric Graphs
There is an increasing interest in the research of clustering or finding communities in complex networks. Graph clustering and graph partitioning algorithms have been applied to this problem. Several graph clustering methods are come into the field but problem lies in the model espoused by the state-of-the-art graph clustering algorithms for solving real-world situation. In this work, an attempt is made to provide an advanced cost based graph clustering algorithm based on stochastic local search. The proposed algorithm delivers significant improvement in robustness and quality of clustering in case of real-world complex network problems. The approach is to compute the cost (scaled cost) accurately when a target node is moved from source to destination cluster.