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Graph stream algorithms: a survey

Author:

Andrew McGregorAuthors Info & Claims

ACM SIGMOD Record, Volume 43, Issue 1

Pages 9 - 20

https://doi.org/10.1145/2627692.2627694

Published: 13 May 2014 Publication History

Abstract

Over the last decade, there has been considerable interest in designing algorithms for processing massive graphs in the data stream model. The original motivation was two-fold: a) in many applications, the dynamic graphs that arise are too large to be stored in the main memory of a single machine and b) considering graph problems yields new insights into the complexity of stream computation. However, the techniques developed in this area are now finding applications in other areas including data structures for dynamic graphs, approximation algorithms, and distributed and parallel computation. We survey the state-of-the-art results; identify general techniques; and highlight some simple algorithms that illustrate basic ideas.

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Guliyev RHaldar AFerhatosmanoglu H(2024)D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural NetworksProceedings of the VLDB Endowment10.14778/3681954.368196117:11(2764-2777)Online publication date: 1-Jul-2024
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Zhang CBonifati AÖzsu M(2024)Incremental Sliding Window Connectivity over Streaming GraphsProceedings of the VLDB Endowment10.14778/3675034.367504017:10(2473-2486)Online publication date: 1-Jun-2024
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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 43, Issue 1

March 2014

71 pages

ISSN:0163-5808

DOI:10.1145/2627692

Editors:
Ioana Manolescu
INRIA Saclay
,
Denilson Barbosa
University of Alberta
,
Pablo Barceló
Universidad de Chile
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Anish Das Sarma
Google Research
,
Glenn Paulley
Conestoga College
,
Alkis Simitsis
HP Labs
,
Nesime Tatbul
ETH Zurich
,
Marianne Winslett
University of Illinois

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Published: 13 May 2014

Published in SIGMOD Volume 43, Issue 1

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Cited By

Guliyev RHaldar AFerhatosmanoglu H(2024)D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural NetworksProceedings of the VLDB Endowment10.14778/3681954.368196117:11(2764-2777)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681961
Zhang CBonifati AÖzsu M(2024)Incremental Sliding Window Connectivity over Streaming GraphsProceedings of the VLDB Endowment10.14778/3675034.367504017:10(2473-2486)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675040
Chen KWen DZhang WZhang YWang XLin X(2024)Querying Structural Diversity in Streaming GraphsProceedings of the VLDB Endowment10.14778/3641204.364121317:5(1034-1046)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641213
Czumaj AMishra GMukherjee AKuznetsov PGelles ROlivetti D(2024)Streaming Graph Algorithms in the Massively Parallel Computation ModelProceedings of the 43rd ACM Symposium on Principles of Distributed Computing10.1145/3662158.3662770(496-507)Online publication date: 17-Jun-2024
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Bhore SFiltser AKhodabandeh HTóth C(2024)Online Spanners in Metric SpacesSIAM Journal on Discrete Mathematics10.1137/22M153457238:1(1030-1056)Online publication date: 12-Mar-2024
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