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Sublinear-Time Opin...
Sublinear-Time Opinion Estimation in the Friedkin - Johnsen Model
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- Neumann, Stefan (author)
- KTH,Teoretisk datalogi, TCS,TU Wien Vienna, Austria
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- Dong, Yinhao (author)
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
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- Peng, Pan (author)
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
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(creator_code:org_t)
- Association for Computing Machinery (ACM), 2024
- 2024
- English.
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In: WWW 2024 - Proceedings of the ACM Web Conference. - : Association for Computing Machinery (ACM). ; , s. 2563-2571
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Online social networks are ubiquitous parts of modern societies and the discussions that take place in these networks impact people's opinions on diverse topics, such as politics or vaccination. One of the most popular models to formally describe this opinion formation process is the Friedkin - Johnsen (FJ) model, which allows to define measures, such as the polarization and the disagreement of a network. Recently, Xu, Bao and Zhang (WebConf'21) showed that all opinions and relevant measures in the FJ model can be approximated in near-linear time. However, their algorithm requires the entire network and the opinions of all nodes as input. Given the sheer size of online social networks and increasing data-access limitations, obtaining the entirety of this data might, however, be unrealistic in practice. In this paper, we show that node opinions and all relevant measures, like polarization and disagreement, can be efficiently approximated in time that is sublinear in the size of the network. Particularly, our algorithms only require query-access to the network and do not have to preprocess the graph. Furthermore, we use a connection between FJ opinion dynamics and personalized PageRank, and show that in d-regular graphs, we can deterministically approximate each node's opinion by only looking at a constant-size neighborhood, independently of the network size. We also experimentally validate that our estimation algorithms perform well in practice.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- friedkin - johnsen model
- opinion formation
- social-network analysis
- sublinear time algorithms
Publication and Content Type
- ref (subject category)
- kon (subject category)
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