Large scale graph processing systems: survey and an experimental evaluation

Batarfi, Omar; Shawi, Radwa El; Fayoumi, Ayman G.; Nouri, Reza; Beheshti, Amin; Barnawi, Ahmed; Sakr, Sherif

doi:10.1007/s10586-015-0472-6

Cited by 99 publications

(53 citation statements)

References 32 publications

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“…The execution time is faster in block-centric, but the overall response time is slower due to overheads discussed in Section 5.1. [13] It was previously reported that GraphX has the best performance across all systems [13]. Our results contradicts this assertion.…”

Section: Related Workcontrasting

confidence: 56%

Experimental analysis of distributed graph systems

Ammar

Özsu

2018

Proc. VLDB Endow.

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This paper evaluates eight parallel graph processing systems: Hadoop, HaLoop, Vertica, Giraph, GraphLab (Pow-erGraph), Blogel, Flink Gelly, and GraphX (SPARK) over four very large datasets (Twitter, World Road Network, UK 200705, and ClueWeb) using four workloads (PageRank, WCC, SSSP and K-hop). The main objective is to perform an independent scale-out study by experimentally analyzing the performance, usability, and scalability (using up to 128 machines) of these systems. In addition to performance results, we discuss our experiences in using these systems and suggest some system tuning heuristics that lead to better performance.

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Section: Related Workcontrasting

confidence: 56%

Experimental analysis of distributed graph systems

Ammar

Özsu

2018

Proc. VLDB Endow.

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“…There are also numerous surveys in the literature studying different topics related to graph processing. Examples include surveys on query languages for graph database systems and RDF engines [10,44,47], graph algorithms [3,45,53,97], graph processing systems [16,61], and visualization [24,95]. These surveys do not study how users use the technologies in practice.…”

Section: Related Workmentioning

confidence: 99%

The ubiquity of large graphs and surprising challenges of graph processing: extended survey

et al. 2019

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Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research.

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“…Simple graphs Attributed graphs Keyword Location Temporal Influence (weight) Profile k-core [175,46,15,66] (P. 1, 2, 3, 4, 5) [61,58] (P. 6) [60,65,185,221] (P. 7,8,9) [129] (P. 10) [127,128,30,215,21,126] (P. 12,13) [31] (P. 14) k-truss [98,6,101] (P. 15,16) [102] (P. 17) -- [216] (P. 18) k-clique [45,205,195,187] (P. 19,20,21,22) -- [125] Example 2 Let us reconsider the graph G in Fig. 2(a).…”

Section: Metricmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

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With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works.In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze

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Large scale graph processing systems: survey and an experimental evaluation

Cited by 99 publications

References 32 publications

Experimental analysis of distributed graph systems

Experimental analysis of distributed graph systems

The ubiquity of large graphs and surprising challenges of graph processing: extended survey

A survey of community search over big graphs

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