research-article

Fast influence-based coarsening for large networks

Authors:

Manish Purohit,

B. Aditya Prakash,

V.S. SubrahmanianAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1296 - 1305

https://doi.org/10.1145/2623330.2623701

Published: 24 August 2014 Publication History

Abstract

Given a social network, can we quickly 'zoom-out' of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications.

In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective near-linear-time (in nodes and edges) algorithm COARSENET for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of COARSENET, enabling us to reduce the graph by 90% in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.

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Cited By

Zhou HLai PSun ZChen XChen YWu HWang Y(2024)AdaMotif: Graph Simplification via Adaptive Motif DesignIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632131:1(688-698)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456321
Jaouadi MBen Romdhane L(2024)A Graph Sampling-Based Model for Influence Maximization in Large-Scale Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321658711:1(144-160)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3216587
Jaouadi MBen Romdhane L(2024)A survey on influence maximization modelsExpert Systems with Applications10.1016/j.eswa.2024.123429248(123429)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.123429
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Index Terms

Fast influence-based coarsening for large networks
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
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Program Chairs:
Jure Leskovec
Stanford University
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Wei Wang
UCLA
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Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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VT College of Engineering
Army Research Office
Division of Information and Intelligent Systems
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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhou HLai PSun ZChen XChen YWu HWang Y(2024)AdaMotif: Graph Simplification via Adaptive Motif DesignIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632131:1(688-698)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456321
Jaouadi MBen Romdhane L(2024)A Graph Sampling-Based Model for Influence Maximization in Large-Scale Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321658711:1(144-160)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3216587
Jaouadi MBen Romdhane L(2024)A survey on influence maximization modelsExpert Systems with Applications10.1016/j.eswa.2024.123429248(123429)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.123429
Kumar MSharma ASaxena SKumar SKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Featured graph coarsening with similarity guaranteesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619148(17953-17975)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619148
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https://dl.acm.org/doi/10.1145/3626724
Zhou YRathore APurvine EWang B(2023)Topological Simplifications of HypergraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.315389529:7(3209-3225)Online publication date: 1-Jul-2023
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Chia CTkachenko MLauw H(2023)Semantically Constitutive Entities in Knowledge GraphsDatabase and Expert Systems Applications10.1007/978-3-031-39847-6_36(445-461)Online publication date: 18-Aug-2023
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Zhao ZFeng Z(2022)A Multilevel Spectral Framework for Scalable Vectorless Power/Thermal Integrity VerificationACM Transactions on Design Automation of Electronic Systems10.1145/352953428:1(1-25)Online publication date: 10-Dec-2022
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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
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