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MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas
  • Devendra Dahiphale
Devendra Dahiphale
University of Maryland Baltimore County, University of Maryland Baltimore County, University of Maryland Baltimore County

Corresponding Author:[email protected]

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Abstract

Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. A deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-Edge Connected Components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithm. Processing a big graph in a parallel and distributed fashion will save considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. In this research, we propose a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ”BiECCA”) and present its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework. The proposed algorithm uses an existing algorithm to find Connected Components (CCs) in a graph as a sub- step. Finally, we suggest a few novel ideas and approaches as extensions to our work.
2023Published in IEEE Access volume 11 on pages 54986-55001. 10.1109/ACCESS.2023.3281266