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.
S. Das et al., "Graph Partitioning in Parallelization of Large Scale Networks," Proceedings of the IEEE 41st Conference on Local Computer Networks (2016, Dubai, United Arab Emirates), pp. 176-179, IEEE Computer Society, Nov 2016.
The definitive version is available at https://doi.org/10.1109/LCN.2016.36
IEEE 41st Conference on Local Computer Networks, LCN 2016 (2016: Nov. 7-10, Dubai, United Arab Emirates)
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)
Article - Conference proceedings
© 2016 Sima Das. Under license to IEEE., All rights reserved.