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Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages

Authors:

Chloe Wohlgemuth,

Matteo RiondatoAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 196 - 206

https://doi.org/10.1145/3447548.3467354

Published: 14 August 2021 Publication History

Abstract

We present Bavarian, a collection of sampling-based algorithms for approximating the Betweenness Centrality (BC) of all vertices in a graph. Our algorithms use Monte-Carlo Empirical Rademacher Averages (MCERAs), a concept from statistical learning theory, to efficiently compute tight bounds on the maximum deviation of the estimates from the exact values. The MCERAs provide a sample-dependent approximation guarantee much stronger than the state of the art, thanks to its use of variance-aware probabilistic tail bounds. The flexibility of the MCERA allows us to introduce a unifying framework that can be instantiated with existing sampling-based estimators of BC, thus allowing a fair comparison between them, decoupled from the sample-complexity results with which they were originally introduced. Additionally, we prove novel sample-complexity results showing that, for all estimators, the sample size sufficient to achieve a desired approximation guarantee depends on the vertex-diameter of the graph, an easy-to-bound characteristic quantity. We also show progressive-sampling algorithms and extensions to other centrality measures, such as percolation centrality. Our extensive experimental evaluation of Bavarian shows the improvement over the state-of-the art made possible by the MCERA, and it allows us to assess the different trade-offs between sample size and accuracy guarantee offered by the different estimators.

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Wang XWang YLin XYu JGao HCheng XYu D(2024)Efficient Betweenness Centrality Computation over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368200617:11(3360-3372)Online publication date: 30-Aug-2024
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Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

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Published: 14 August 2021

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Cited By

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https://dl.acm.org/doi/10.1007/s11704-023-3424-y
Wang XWang YLin XYu JGao HCheng XYu D(2024)Efficient Betweenness Centrality Computation over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368200617:11(3360-3372)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3682006
Zhang TGao YZhao JChen LJin LYang ZCao BFan JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficient Exact and Approximate Betweenness Centrality Computation for Temporal GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645438(2395-2406)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645438
Cruciani A(2024)MANTRA: Temporal Betweenness Centrality Approximation Through SamplingMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70341-6_8(125-143)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70341-6_8
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
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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
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Simionato DVandin F(2022)Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher AveragesMachine Learning and Knowledge Discovery in Databases10.1007/978-3-031-26419-1_16(255-271)Online publication date: 19-Sep-2022
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