Graph Partitioning in Parallelization of Large Scale Networks


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.

Meeting Name

IEEE 41st Conference on Local Computer Networks, LCN 2016 (2016: Nov. 7-10, Dubai, United Arab Emirates)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Graphic methods; Community structures; Computational task; Graph Partitioning; Large-scale network; Parallelizations; Partitioning algorithms; Real-world; Real-world networks; Computer networks

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2016 Sima Das. Under license to IEEE., All rights reserved.

Publication Date

01 Nov 2016