Percolation Threshold for Competitive Influence in Random Networks
| Autor: | Duan-Shin Lee, Yu-Hsien Peng, Ping-En Lu, Cheng-Shang Chang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Computer Science::Computer Science and Game Theory Computer science media_common.quotation_subject FOS: Physical sciences 02 engineering and technology Physics and Society (physics.soc-ph) 010501 environmental sciences 01 natural sciences Order (exchange) Stochastic block model 020204 information systems Voting 0202 electrical engineering electronic engineering information engineering Statistical physics 0105 earth and related environmental sciences media_common Random graph Social and Information Networks (cs.SI) Stochastic process Percolation threshold Computer Science - Social and Information Networks Human-Computer Interaction Influence propagation Computer Science::Multiagent Systems Modeling and Simulation Social Sciences (miscellaneous) |
| DOI: | 10.48550/arxiv.1904.05754 |
| Popis: | In this paper, we propose a new averaging model for modeling the competitive influence of $K$ candidates among $n$ voters in an election process. For such an influence propagation model, we address the question of how many seeded voters a candidate needs to place among undecided voters in order to win an election. We show that for a random network generated from the stochastic block model, there exists a percolation threshold for a candidate to win the election if the number of seeded voters placed by the candidate exceeds the threshold. By conducting extensive experiments, we show that our theoretical percolation thresholds are very close to those obtained from simulations for random networks and the errors are within $10\%$ for a real-world network. Comment: 11 pages, 9 figures, this article is the complete version (with proofs) of the IEEE Global Communications Conference 2019 review paper |
| Databáze: | OpenAIRE |
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