article

Big Graph Analytics Platforms

Authors:

Amol DeshpandeAuthors Info & Claims

Foundations and Trends in Databases, Volume 7, Issue 1-2

Pages 1 - 195

https://doi.org/10.1561/1900000056

Published: 12 January 2017 Publication History

Abstract

Due to the growing need to process large graph and network datasetscreated by modern applications, recent years have witnessed a surginginterest in developing big graph platforms. Tens of such big graphsystems have already been developed, but there lacks a systematic categorizationand comparison of these systems. This article provides atimely and comprehensive survey of existing big graph systems, andsummarizes their key ideas and technical contributions from variousaspects. In addition to the popular vertex-centric systems which espousea think-like-a-vertex paradigm for developing parallel graph applications,this survey also covers other programming and computationmodels, contrasts those against each other, and provides a vision forthe future research on big graph analytics platforms. This survey aimsto help readers get a systematic picture of the landscape of recent biggraph systems, focusing not just on the systems themselves, but alsoon the key innovations and design philosophies underlying them.

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Cited By

Zhang ZLu YZheng WLin X(2024)A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and InteractionProceedings of the ACM on Management of Data10.1145/36393152:1(1-29)Online publication date: 26-Mar-2024
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Yan DYuan LAhmad AAdhikari SSerra ESpezzano F(2024)Systems for Scalable Graph Analytics and Machine Learning: Trends and MethodsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679101(5547-5550)Online publication date: 21-Oct-2024
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Bonifati AÖzsu MTian YVoigt HYu WZhang WBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)The Future of Graph AnalyticsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3658369(544-545)Online publication date: 9-Jun-2024
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cover image Foundations and Trends in Databases

Foundations and Trends in Databases Volume 7, Issue 1-2

12 1 2017

199 pages

ISSN:1931-7883

EISSN:1931-7891

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Now Publishers Inc.

Hanover, MA, United States

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Published: 12 January 2017

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Zhang ZLu YZheng WLin X(2024)A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and InteractionProceedings of the ACM on Management of Data10.1145/36393152:1(1-29)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639315
Yan DYuan LAhmad AAdhikari SSerra ESpezzano F(2024)Systems for Scalable Graph Analytics and Machine Learning: Trends and MethodsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679101(5547-5550)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679101
Bonifati AÖzsu MTian YVoigt HYu WZhang WBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)The Future of Graph AnalyticsCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3658369(544-545)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3658369
De Leo DBoncz P(2021)Teseo and the analysis of structural dynamic graphsProceedings of the VLDB Endowment10.14778/3447689.344770814:6(1053-1066)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.14778/3447689.3447708
Gévay GSoto JMarkl V(2021)Handling Iterations in Distributed Dataflow SystemsACM Computing Surveys10.1145/347760254:9(1-38)Online publication date: 8-Oct-2021
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Smagulova ADeutsch ALi GLi ZIdreos SSrivastava D(2021)Vertex-centric Parallel Computation of SQL QueriesProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457314(1664-1677)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457314
Lu SSun SPaul JLi YHe BLi GLi ZIdreos SSrivastava D(2021)Cache-Efficient Fork-Processing Patterns on Large GraphsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457253(1208-1221)Online publication date: 9-Jun-2021
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Ko SLee THong KLee WSeo ISeo JHan WLi GLi ZIdreos SSrivastava D(2021)iTurboGraphProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457243(977-990)Online publication date: 9-Jun-2021
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Dhulipala LBlelloch GShun J(2021)Theoretically Efficient Parallel Graph Algorithms Can Be Fast and ScalableACM Transactions on Parallel Computing10.1145/34343938:1(1-70)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3434393
Khalil JYan DGuo GYuan L(2021)Parallel mining of large maximal quasi-cliquesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00712-231:4(649-674)Online publication date: 26-Nov-2021
https://dl.acm.org/doi/10.1007/s00778-021-00712-2
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