Graph Partitioning in Parallelization of Large Scale Networks

Abstract

Real world large scale networks exhibit intrinsic community structure, with dense intra-community connectivity and sparse inter-community connectivity. Leveraging their community structure for parallelization of computational tasks and applications, is a significant step towards computational efficiency and application effectiveness. We propose a weighted depth-first-search graph partitioning algorithm for community formation that preserves the needed community dependency without any cycles. To comply with heterogeneity in community structure and size of the real world networks, we use a flexible limiting value for them. Further, our algorithm is a diversion from the existing modularity based algorithms. We evaluate our algorithm as the quality of the generated partitions, measured in terms of number of graph cuts.