Lin, M, Li, W, Song, LJ orcid.org/0000-0002-0969-4091 et al. (3 more authors) (2021) SAKE: Estimating Katz Centrality Based on Sampling for Large-Scale Social Networks. ACM Transactions on Knowledge Discovery from Data, 15 (4). 66. ISSN 1556-4681
Abstract
Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network are unpractical and computationally expensive. In this article, we propose a novel method to estimate Katz centrality based on graph sampling techniques, which object to achieve comparable estimation accuracy of the state-of-the-arts with much lower computational complexity. Specifically, we develop a Horvitz–Thompson estimate for Katz centrality by using a multi-round sampling approach and deriving an unbiased mean value estimator. We further propose SAKE, a Sampling-based Algorithm for fast Katz centrality Estimation. We prove that the estimator calculated by SAKE is probabilistically guaranteed to be within an additive error from the exact value. Extensive evaluation experiments based on four real-world networks show that the proposed algorithm can estimate Katz centralities for partial vertices with low sampling rate, low computation time, and it works well in identifying high influence vertices in social networks.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 ACM. This is an author produced version of an article published in ACM Transactions on Knowledge Discovery from Data. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Dec 2020 11:21 |
Last Modified: | 07 Jun 2023 15:02 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3441646 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168959 |