research-article

Estimating the Percolation Centrality of Large Networks through Pseudo-dimension Theory

Authors:

Alane M. de Lima,

Murilo V. G. da Silva,

André L. VignattiAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1839 - 1847

https://doi.org/10.1145/3394486.3403235

Published: 20 August 2020 Publication History

Abstract

In this work we investigate the problem of estimating the percolation centrality of every vertex in a graph. This centrality measure quantifies the importance of each vertex in a graph going through a contagious process. It is an open problem whether the percolation centrality can be computed in O(n3-c) time, for any constant c>0. In this paper we present a ~O(m) randomized approximation algorithm for the percolation centrality for every vertex of G, generalizing techniques developed by Riondato, Upfal and Kornaropoulos. The estimation obtained by the algorithm is within ε of the exact value with probability 1- δ, for fixed constants 0 < ε,δ < 1. In fact, we show in our experimental analysis that in the case of real-world complex networks, the output produced by our algorithm is significantly closer to the exact values than its guarantee in terms of theoretical worst case analysis.

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Cited By

Pellegrina LVandin F(2023) SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher BoundsACM Transactions on Knowledge Discovery from Data10.1145/362860118:3(1-55)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3628601
Pellegrina LSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Efficient Centrality Maximization with Rademacher AveragesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599325(1872-1884)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599325
Cousins CWohlgemuth CRiondato M(2023) Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher AveragesACM Transactions on Knowledge Discovery from Data10.1145/357702117:6(1-47)Online publication date: 6-Mar-2023
https://dl.acm.org/doi/10.1145/3577021
Show More Cited By

Index Terms

Estimating the Percolation Centrality of Large Networks through Pseudo-dimension Theory
1. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
    2. Graph algorithms analysis
      1. Shortest paths
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Sample complexity and generalization bounds

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cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

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Published: 20 August 2020

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National Council for Scientific and Technological Development (CNPq)
Coordination for the Improvement of Higher Education Personnel (CAPES)

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

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Cited By

Pellegrina LVandin F(2023) SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher BoundsACM Transactions on Knowledge Discovery from Data10.1145/362860118:3(1-55)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3628601
Pellegrina LSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Efficient Centrality Maximization with Rademacher AveragesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599325(1872-1884)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599325
Cousins CWohlgemuth CRiondato M(2023) Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher AveragesACM Transactions on Knowledge Discovery from Data10.1145/357702117:6(1-47)Online publication date: 6-Mar-2023
https://dl.acm.org/doi/10.1145/3577021
de Lima Ada Silva MVignatti A(2022)Percolation centrality via Rademacher ComplexityDiscrete Applied Mathematics10.1016/j.dam.2021.07.023323:C(201-216)Online publication date: 31-Dec-2022
https://dl.acm.org/doi/10.1016/j.dam.2021.07.023
de Lima Ada Silva MVignatti A(2022)Estimating the Clustering Coefficient Using Sample Complexity AnalysisLATIN 2022: Theoretical Informatics10.1007/978-3-031-20624-5_20(328-341)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-20624-5_20
Cousins CWohlgemuth CRiondato MZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)BavarianProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467354(196-206)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467354

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