Information diffusion modeling and analysis for socially interacting networks

Kumar, Pawan; Sinha, Adwitiya

doi:10.1007/s13278-020-00719-7

Cited by 60 publications

(22 citation statements)

References 33 publications

(43 reference statements)

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“…The directed Twitter graph produced contains a total of 36,118 edges, of which 4571 are self‐loops. Self‐loops in Twitter social network signifies the status message, that is, a tweet by any user which is not replied or mentioned by any other user in the network 39,40 …”

Section: Implementation Results and Analysismentioning

confidence: 99%

Discovering influential users in social network using weighted cumulative centrality

Jain

Sinha

2021

Concurrency and Computation

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The sharing of information is an inherent tendency of human beings that helps in content dissemination as it cascades to the larger networked audience. In this article, we have proposed a novel weighted cumulative measure for discovering influential users in such networked structures. Our method generically combines the impact of multiple structural features of the network, along with local and global information to produce weighted cumulative centrality (WCC) score. Local information internments nodes direct neighborhood influence whereas global information scrutinizes network structure as a whole via propagating shortest paths. To assess the performance of our innovated measure, a benchmark diffusion model is employed as an artificial stochastic model to track the rate of information spread. The resultant outcome is verified on five different networks including scale‐free and random, and real‐world networks, namely, Facebook, BlogCatalog, and Twitter network. Furthermore, a comparative study is conducted to justify the spreading ability of proposed multifeature based WCC against existing single‐feature based approaches using relative performance ratio. Simulation with single and multiple triggering nodes endorse the efficacy of our proposed method over existing counterparts in terms of the coverage rate, thus showing support for early adoption phenomena in proposed WCC to infest a social network.

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Section: Implementation Results and Analysismentioning

confidence: 99%

Discovering influential users in social network using weighted cumulative centrality

Jain

Sinha

2021

Concurrency and Computation

Self Cite

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“…This in turn would increase the utility of our methods for realtime inference during an outbreak. Furthermore, it would also be of interest to demonstrate the utility of our modeling framework in different contexts beyond spatial epidemic models, such as static network diffusion processes (30).…”

Section: Discussionmentioning

confidence: 99%

Bayesian nonparametric inference for heterogeneously mixing infectious disease models

Seymour

Kypraios

O’Neill

2022

Proc. Natl. Acad. Sci. U.S.A.

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Significance Mathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

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“…Information diffusion in online social networks is similar to the way the virus spreads in a population [ 16 ]. There are a few recent works in the literature that attempt to model the external influence in information diffusion in online social networks [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection

Sivaraman

Baijal

Muthiah

et al. 2022

Int J Data Sci Anal

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Epidemics like Covid-19 and Ebola have impacted people’s lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc., is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is influenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic.

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Information diffusion modeling and analysis for socially interacting networks

Cited by 60 publications

References 33 publications

Discovering influential users in social network using weighted cumulative centrality

Discovering influential users in social network using weighted cumulative centrality

Bayesian nonparametric inference for heterogeneously mixing infectious disease models

Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection

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