Power-Law Distributed Graph Generation With MapReduce

Angles, Renzo; Lopez-Gallegos, Fernanda; Paredes, Rodrígo

doi:10.1109/access.2021.3093329

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Furthermore, the previous work for graph generation with real-world properties can be leveraged using synthetic graphs. There are many graph generators based on the MapReduce framework, for example, Power-Law Distributed Graph Generation With MapReduce [39], A Rapid and Robust Graph Generator [40], TrillionG: A trillion-scale synthetic graph generator using a recursive vector model [41], Distributed Tera-Scale Graph Generation and Visualization [42] etc.…”

Section: Future Scopementioning

confidence: 99%

MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

Dahiphale

2023

IEEE Access

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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. The 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 algorithms. Processing a big graph in a parallel and distributed fashion saves considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. This research proposes a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ''BiECCA ′′ ) and presents its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework and 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.

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Section: Future Scopementioning

confidence: 99%

MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

Dahiphale

2023

IEEE Access

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“…Pregel computations consist of a sequence of iterations, called supersteps. Parallels such as MapReduce [19][20][21], which is a programming model for processing large amounts of data in distributed parallel computing, simplify the design and implementation of large-capacity data processing systems; however, they may result in inefficient processing because they do not support efficient data mining and machine learning algorithms. To resolve this problem, GraphLab has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Bok,

Kim,

Lee

et al. 2023

Electronics

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As various services have been generating large-scale graphs to represent multiple relationships between objects, studies have been conducted to obtain subgraphs with particular patterns. In this paper, we propose a distributed query processing method to efficiently search a subgraph for a large graph on Spark. To reduce unnecessary processing costs, the search order is determined by filtering scores using the probability distribution. The partitioned queries are searched in parallel in the distributed graph of each slave node according to the search order, and the local search results obtained from each slave node are combined and returned. The query is partitioned in triplets based on the determined search order. The performance of the proposed method is compared with the performance of existing methods to demonstrate its superiority.

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