Article

Graph Summarization with Quality Guarantees

Authors:

Matteo Riondato,

David García-Soriano,

Francesco BonchiAuthors Info & Claims

ICDM '14: Proceedings of the 2014 IEEE International Conference on Data Mining

Pages 947 - 952

https://doi.org/10.1109/ICDM.2014.56

Published: 14 December 2014 Publication History

Abstract

We study the problem of graph summarization. Given a large graph we aim at producing a concise lossy representation that can be stored in main memory and used to approximately answer queries about the original graph much faster than by using the exact representation. In this paper we study a very natural type of summary: the original set of vertices is partitioned into a small number of super nodes connected by super edges to form a complete weighted graph. The super edge weights are the edge densities between vertices in the corresponding super nodes. The goal is to produce a summary that minimizes the reconstruction error w.r.t. The original graph. By exposing a connection between graph summarization and geometric clustering problems (i.e., k-means and k-median), we develop the first polynomial-time approximation algorithm to compute the best possible summary of a given size.

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Yang XSong CGu JLi KLi H(2023)A distributed streaming framework for edge–cloud triangle counting in graph streamsKnowledge-Based Systems10.1016/j.knosys.2023.110878278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110878
Ke XKhan ABonchi F(2022)Multi-relation Graph SummarizationACM Transactions on Knowledge Discovery from Data10.1145/349456116:5(1-30)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3494561
Yong QHajiabadi MSrinivasan VThomo ALi GLi ZIdreos SSrivastava D(2021)Efficient Graph Summarization using Weighted LSH at Billion-ScaleProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457331(2357-2365)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457331
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Graph Summarization with Quality Guarantees
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Published In

ICDM '14: Proceedings of the 2014 IEEE International Conference on Data Mining

December 2014

1144 pages

ISBN:9781479943029

Publisher

IEEE Computer Society

United States

Publication History

Published: 14 December 2014

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Cited By

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Yang XSong CGu JLi KLi H(2023)A distributed streaming framework for edge–cloud triangle counting in graph streamsKnowledge-Based Systems10.1016/j.knosys.2023.110878278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110878
Ke XKhan ABonchi F(2022)Multi-relation Graph SummarizationACM Transactions on Knowledge Discovery from Data10.1145/349456116:5(1-30)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3494561
Yong QHajiabadi MSrinivasan VThomo ALi GLi ZIdreos SSrivastava D(2021)Efficient Graph Summarization using Weighted LSH at Billion-ScaleProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457331(2357-2365)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457331
Hajiabadi MSingh JSrinivasan VThomo AZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Graph Summarization with Controlled Utility LossProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467359(536-546)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467359
Ahmad MAli STariq JKhan IShabbir MZaman A(2020)Combinatorial trace method for network immunizationInformation Sciences: an International Journal10.1016/j.ins.2020.01.037519:C(215-228)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1016/j.ins.2020.01.037
Liu YSafavi TDighe AKoutra D(2018)Graph Summarization Methods and ApplicationsACM Computing Surveys10.1145/318672751:3(1-34)Online publication date: 22-Jun-2018
https://dl.acm.org/doi/10.1145/3186727
Hao FPark D(2018)cSketchThe Journal of Supercomputing10.1007/s11227-017-2114-774:3(1202-1214)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11227-017-2114-7
Khan KNawaz WLee Y(2017)Set-based unified approach for summarization of a multi-attributed graphWorld Wide Web10.1007/s11280-016-0388-y20:3(543-570)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s11280-016-0388-y
van Heeswijk WFletcher GPechenizkiy MOssowski S(2016)On structure preserving sampling and approximate partitioning of graphsProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851650(875-882)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851650

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