Graph Computing Systems and Partitioning Techniques: A Survey

Ayall, Tewodros Alemu; Liu, Huawen; Zhou, Changjun; Seid, Abegaz Mohammed; Gereme, Fantahun Bogale; Abishu, Hayla Nahom; Yacob, Yasin Habtamu

doi:10.1109/access.2022.3219422

Cited by 15 publications

(3 citation statements)

References 193 publications

(227 reference statements)

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“…There are many approaches to partition [34] graphs. Because the output of the MapReduce Job-1 is an edge list, it can be split into multiple chunks, where each chunk can have a set of edges.…”

Section: All Implemented Mapreduce Jobs 1) Adjacency List To Edge Lis...mentioning

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: All Implemented Mapreduce Jobs 1) Adjacency List To Edge Lis...mentioning

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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“…mining [1] and is the basis for various applications,including community mining [2], picture feature selection [3], decision making [4], graph computing systems [5], and action recognition [6] and so on.…”

Section: Introductionmentioning

confidence: 99%

Efficient Densest Subgraphs Discovery in Large Dynamic Graphs by Greedy Approximation

Han¹

2023

IEEE Access

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Densest subgraph detection has become an important primitive in graph mining tasks when analyzing communities and detecting events in a wide range of application domains. Currently, it is a challenging and practically crucial research issue to develop efficient densest subgraphs mining approaches that can handle both very large and continuously evolving graphs. Although large-scale or dynamic methods have been proposed to find the densest subgraphs, there is still a lack of a promising method to deal with largescale and dynamically evolving graphs. In this paper, the problem is formulated and proved to be NP-Hard, an incremental greedy approximation approach is proposed, and its running time is O(m+n). In order to find the densest subgraph effectively by heuristically merging the local densest subgraphs, firstly, the edge flow of a dynamic graph is divided into several subgraphs in a given period of T . Secondly, a local candidate set is generated by local denser subgraph discovery. Third, the global densest subgraph candidates are collected by heuristically merging. Last, the densest subgraphs are induced from the global densest subgraph candidates with constraints by static densest subgraph discovery algorithm. This incremental approach enables us to scale up the existing densest subgraph discovery algorithms, which focus mainly on small and static graphs and thus can handle very large dynamic graphs. Experiments on real-world networks with billions of nodes for comprehensive evaluations present excellent improvement in efficiency and accuracy: it reduces about 25% running time on average and presents a more accurate estimation of the structure of a graph with more compact subgraphs than the static method.It also performs well when dealing with graphs of varying densities. INDEX TERMSDensest subgraphs, incremental greedy approximation, heuristically merging, local candidate, global densest subgraph candidates.

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“…In the IoT, for example, data transmission and control flows among connected devices ("things") are modeled as graphs that are analyzed to identify anomalies or to group things used by the interactions. Since large amounts of graphs have been generated in respect of social media, the IoT, and so on, systems have been developed to partition and store graphs to perform distributed processing [9][10][11][12].…”

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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Graph Computing Systems and Partitioning Techniques: A Survey

Cited by 15 publications

References 193 publications

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

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

Efficient Densest Subgraphs Discovery in Large Dynamic Graphs by Greedy Approximation

Distributed Subgraph Query Processing Using Filtering Scores on Spark

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