SPIR: The potential spreaders involved SIR model for information diffusion in social networks

Rui, Xiaobin; Meng, Fei; Wang, Zhixiao; Yuan, Guan; Du, Changjiang

doi:10.1016/j.physa.2018.04.062

Cited by 45 publications

(24 citation statements)

References 27 publications

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“…Infection spread models from epidemiology, namely SIR (Susceptible, Infected, Recovered) [21], SIS (Susceptible, Infected, Susceptible) [15], SEIZ (susceptible, exposed, infected, skeptic) [44], SIHR (Spreaders, Ignorants, Hibernators, Removed) [33] and their variants [45,44,43,46,47] have been widely used to model information spreading, including rumors. Modelling rumor spreading as cascade structures in social networks is also well studied [9,31].…”

Section: Rumor Spreading Modelsmentioning

confidence: 99%

Evaluating vulnerability to fake news in social networks

Rath

Gao

Srivastava

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individuallevel) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using three popular community detection algorithms for twelve real-world news spreading networks collected from Twitter. Experimental results show that the proposed metrics perform significantly better on the fake news spreading networks than on the true news, indicating that our community health assessment model is effective.

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Section: Rumor Spreading Modelsmentioning

confidence: 99%

Evaluating vulnerability to fake news in social networks

Rath

Gao

Srivastava

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…error [2] ← error [3] , σ [2] ← σ [3] 20: end while 21: Output u [2] where f (•) : R → R is a function of σ. It yields that σ is the root when the error equals zero.…”

Section: Solution Techniquesmentioning

confidence: 99%

“…Information epidemics, which is analogous to epidemics spreading in populations, describes the information dissemination in social networks [1,2]. Thus the epidemics models are introduced to the field of information diffusion [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Robust optimal control of deterministic information epidemics with noisy transition rates

Liu

Zhang

Buss

2019

Physica A: Statistical Mechanics and its Applications

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In this paper the robust optimal control of deterministic information epidemics is inspected taking into consideration the noisy transition rates. Distinct from conventional works, the heterogeneous susceptible-infected-susceptible (SIS) model is adopted where both the heterogeneities in the network topology and the individual diversity are considered. In light of the commonly existing noise in the transition processes, we address the robust optimal control problem aiming at maximizing the spreading performance at the finite time instant given a fixed budget. By using the distribution analysis techniques, the inspected problem is transformed to a constrained optimal control problem and solved by the Pontryagin Maximum Principle (PMP). A novel approach combining the forward backward sweep method and the secant method is proposed to efficiently reduce the computation burden. The performance of the robust optimal control as well as the influence of the parameters is examined by numerical experiments in real social networks.

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“…Rui et al. [17] proposed a susceptible-potential-infective-removed (SPIR) model introducing a potential spreader set, which made the state-changing mechanism more reasonable and accurate for the diffusion process.…”

Section: Introductionmentioning

confidence: 99%