Top-k node identification method based on Gaussian plume model
Authors: 
Xu Cao, Feng YinAuthors Info & Claims
Xu Cao, Feng YinAuthors Info & ClaimsArticle No.: 89, Pages 1 - 6
Abstract
As one of the most commonly used models in the Top-k node recognition task, the greedy model has the advantages of convenience, easy understanding and stable effect. The CELF++ algorithm, as a method of using the greedy strategy, also has the above characteristics. However, since the algorithm uses Monte Carlo simulation to calculate the effect of node influence diffusion, its time overhead is unbearable on large networks. Regarding the above points, this paper introduces a Gaussian plume model commonly used in the field of atmospheric pollution diffusion simulation, and proposes a Gaussian influence diffusion model. On this basis, the CELF++ algorithm is improved, and the Gaussian influence diffusion model is used to replace the traditional Monte Carlo simulation to model the influence diffusion in social networks, and the GPM-CELF++ (Gaussian Plume Model-CELF++) algorithm is proposed. Extensive experimental results on real datasets show that the proposed algorithm has advantages in both propagation effect and running time compared with baseline methods.
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Published In
December 2022
770 pages
ISBN:9781450398336
DOI:10.1145/3579654
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 March 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- Chengdu philosophy and social science planning project
- The Fundamental Research Funds for The Central Universities of Southwest Minzu University
- Sichuan Science and Technology Funding Project
- National Social Science Foundation Major Tendering Project
Conference
ACAI 2022
ACAI 2022: 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
December 23 - 25, 2022
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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