research-article

Sparsification of influence networks

Authors:

Michael Mathioudakis,

Francesco Bonchi,

Carlos Castillo,

Aristides Gionis,

Antti UkkonenAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 529 - 537

https://doi.org/10.1145/2020408.2020492

Published: 21 August 2011 Publication History

Abstract

We present Spine, an efficient algorithm for finding the "backbone" of an influence network. Given a social graph and a log of past propagations, we build an instance of the independent-cascade model that describes the propagations. We aim at reducing the complexity of that model, while preserving most of its accuracy in describing the data.

We show that the problem is inapproximable and we present an optimal, dynamic-programming algorithm, whose search space, albeit exponential, is typically much smaller than that of the brute force, exhaustive-search approach. Seeking a practical, scalable approach to sparsification, we devise Spine, a greedy, efficient algorithm with practically little compromise in quality.

We claim that sparsification is a fundamental data-reduction operation with many applications, ranging from visualization to exploratory and descriptive data analysis. As a proof of concept, we use Spine on real-world datasets, revealing the backbone of their influence-propagation networks. Moreover, we apply Spine as a pre-processing step for the influence-maximization problem, showing that computations on sparsified models give up little accuracy, but yield significant improvements in terms of scalability.

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Cited By

Sun ZZeng G(2024)A Two-stage Coarsening Method for a Streaming Graph with Preserving Key FeaturesProceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security10.1145/3665348.3665392(253-260)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3665348.3665392
Wen ZLiu YTan SChen JZhu MHan DYin JXu MChen W(2024)Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332714830:1(573-583)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327148
Li YGao HGao YGuo JWu W(2023)A Survey on Influence Maximization: From an ML-Based Combinatorial OptimizationACM Transactions on Knowledge Discovery from Data10.1145/360455917:9(1-50)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3604559
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Index Terms

Sparsification of influence networks
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 ACM.

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Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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

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Cited By

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https://dl.acm.org/doi/10.1145/3665348.3665392
Wen ZLiu YTan SChen JZhu MHan DYin JXu MChen W(2024)Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332714830:1(573-583)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327148
Li YGao HGao YGuo JWu W(2023)A Survey on Influence Maximization: From an ML-Based Combinatorial OptimizationACM Transactions on Knowledge Discovery from Data10.1145/360455917:9(1-50)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3604559
Holdsworth DZagorecki A(2023)A picture paints a thousand words: supporting organizational learning in the emergency services with data visualizationThe Learning Organization10.1108/TLO-01-2022-000130:2(231-250)Online publication date: 23-Mar-2023
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Quinn KTerzi ECrovella MZhang ARangwala H(2022)Characterizing Covid Waves via Spatio-Temporal DecompositionProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539136(3783-3791)Online publication date: 14-Aug-2022
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Wickman RIves ZBonifati AEl Abbadi A(2022)SparRL: Graph Sparsification via Deep Reinforcement LearningProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3520254(2521-2523)Online publication date: 10-Jun-2022
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Zhang XSong DTao D(2022)Sparsified Subgraph Memory for Continual Graph Representation Learning2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00177(1335-1340)Online publication date: Nov-2022
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Wickman RZhang XLi W(2022)A Generic Graph Sparsification Framework using Deep Reinforcement Learning2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00158(1221-1226)Online publication date: Nov-2022
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Hayashi TNakamura A(2022)Propagation graph estimation from individuals’ time series of observed statesScientific Reports10.1038/s41598-022-10031-312:1Online publication date: 12-Apr-2022
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Beldi ASassi SChbeir RJemai A(2022)Schema Formalism for Semantic Summary Based on Labeled Graph from Heterogeneous DataRecent Challenges in Intelligent Information and Database Systems10.1007/978-981-19-8234-7_3(27-44)Online publication date: 24-Nov-2022
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