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ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages

Authors:

Matteo Riondato,

Eli UpfalAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 5

Article No.: 61, Pages 1 - 38

https://doi.org/10.1145/3208351

Published: 20 July 2018 Publication History

Abstract

ABPA Ξ AΣ (ABRAXAS): Gnostic word of mystic meaning.

We present ABRA, a suite of algorithms to compute and maintain probabilistically guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, ABRA is the first application of these concepts to the field of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime number of samples, and accuracy, state-of-the-art algorithms with the same quality guarantees.

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ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 12, Issue 5

October 2018

354 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3234931

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

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Copyright © 2018 ACM.

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Publication History

Published: 20 July 2018

Accepted: 01 April 2018

Revised: 01 April 2018

Received: 01 July 2017

Published in TKDD Volume 12, Issue 5

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Brunelli FCrescenzi PViennot LBaeza-Yates RBonchi F(2024)Making Temporal Betweenness Computation Faster and RestlessProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671825(163-174)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671825
Zhang TFang JYang ZCao BFan JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)TATKC: A Temporal Graph Neural Network for Fast Approximate Temporal Katz Centrality RankingProceedings of the ACM Web Conference 202410.1145/3589334.3645432(527-538)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645432
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