A Multi-Feature Diffusion Model: Rumor Blocking in Social Networks

Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit th...

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Vydáno v:IEEE/ACM transactions on networking Ročník 29; číslo 1; s. 386 - 397
Hlavní autoři: Guo, Jianxiong, Chen, Tiantian, Wu, Weili
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
approximation algorithm
Approximation algorithms
Automobiles
Computational modeling
Diffusion barriers
Economic impact
IEEE transactions
martingale
Multi-feature diffusion
Multilayers
rumor blocking
sampling
Sampling methods
Social networking (online)
Social networks
ISSN:1063-6692, 1558-2566
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Shrnutí:Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit the spread of rumors. However, most of the related researches were based on a one-dimensional diffusion model. In reality, there is more than one feature associated with an object. A user's impression on this object is determined not just by one feature but by her overall evaluation of all features associated with it. Thus, the influence spread of this object can be decomposed into the spread of multiple features. Based on that, we design a multi-feature diffusion model (MF-model) in this paper and formulate a multi-feature rumor blocking (MFRB) problem on a multi-layer network structure according to this model. To solve the MFRB problem, we design a creative sampling method called Multi-Sampling, which can be applied to this multi-layer network structure. Then, we propose a Revised-IMM algorithm and obtain a satisfactory approximate solution to MFRB. Finally, we evaluate our proposed algorithm by conducting experiments on real datasets, which shows the effectiveness of our Revised-IMM and its advantage to their baseline algorithms.
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ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2020.3032893