Modeling Epidemics Spreading on Social Contact Networks

Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua

doi:10.1109/tetc.2015.2398353

Cited by 74 publications

(55 citation statements)

References 36 publications

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“…The results obtained from both of the simulations show that diseases spread even more in social contact networks having greater average of degree. Some dormant immunization strategies have been presented in this work as well to support the findings [3].…”

Section: Literature Reviewsupporting

confidence: 68%

“…The existing techniques for evaluating the centrality measures involve a neighborhood-based approach and a shortest path algorithm approach. The neighborhood approach makes use of the key features of a node such as the degree centrality (DC) and Eigenvector centrality (EVC), while the shortest path approach utilizes the betweenness centrality (BC) and the closeness centrality (CC) measures [3].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Algorithm for Maximal Clique Size Evaluation

Fatima¹,

Hina²

2019

IJACSA

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A large dataset network is considered for computation of maximal clique size (MC). Additionally, its link with popular centrality metrics to decrease uncertainty and complexity and for finding influential points of any network has also been investigated. Previous studies focus on centrality metrics like degree centrality (DC), closeness centrality (CC), betweenness centrality (BC) and Eigenvector centrality (EVC) and compare them with maximal clique size however, in this study Katz centrality measure is also considered and shows a pretty robust relation with maximal clique size (MC). Secondly, maximal clique size (MC) algorithm is also revised for network analysis to avoid complexity in computation. Association between MC and five centrality metrics has been evaluated through recognized methods that are Pearson's correlation coefficient (PCC), Spearman's correlation coefficient (SCC) and Kendall's correlation coefficient (KCC). The strong strength of association between them is seen through all three correlation coefficients measure.

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Section: Literature Reviewsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Efficient Algorithm for Maximal Clique Size Evaluation

Fatima¹,

Hina²

2019

IJACSA

View full text Add to dashboard Cite

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“…In recent years, epidemic theory has found applications in various different fields, covering virus/disease spreading (both biological and digital ones) (e.g., [1] [5]) and corresponding immunization strategies (e.g., [26] [27] [28] [29]), information dissemination in (online) social networks (e.g., [30]), communication protocol design (e.g., [31] [32] [33]) and cascading failure prediction/protection (e.g., [34]) as well as in more general contexts, analysis on stability of spreading processes over time-varying networks (e.g., [35]) and iden-tification of influential seeds/spreaders in networks (e.g., [36]). …”

Section: Background Basics and Related Workmentioning

confidence: 99%

Modelling Spreading Process Induced by Agent Mobility in Complex Networks

Chai

2018

IEEE Trans. Netw. Sci. Eng.

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Abstract-Most conventional epidemic models assume contact-based contagion process. We depart from this assumption and study epidemic spreading process in networks caused by agents acting as carrier of infection. These agents traverse from origins to destinations following specific paths in a network and in the process, infecting the sites they travel across. We focus our work on the Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents' vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters' activities in the system. We highlight the key differences in the spreading patterns between the contact-based vs. agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based model due to agents' movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of the network and vice versa.

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“…Early studies on social networks were limited by manual data collection and considered at most hundreds of individuals [39]. Later, social network analysis (SNA) became an interesting topic for many other sectors and research fields, including recommender systems [24], [31]; marketing [7]; intelligence analysis [35]; network structure [16]; modeling epidemics spreading [44]; clustering and community detection [6], [9], [15], [17], [18], [23] and complex systems [19]. Massive use of electronic devices and online communication leaves traces of human interaction and relationships, such as phone call records, e-mail records, etc.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Social Networks Based on Tera-Scale Telecommunication Datasets

Aksu

Korpeoglu

Ulusoy

2019

IEEE Trans. Emerg. Topics Comput.

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With the popularization of mobile phone usage, telecommunication networks have turned into a socially binding medium. Considering the traces of human communication held inside these networks, telecommunication networks are now able to provide a proxy for human social networks. To study degree characteristics and structural properties in large-scale social networks, we gathered a tera-scale dataset of call detail records that contains % 5 Â 10 7 nodes and % 3:6 Â 10 10 links for three GSM (mobile) networks, as well as % 1:4 Â 10 7 nodes and % 1:9 Â 10 9 links for one PSTN (fixed-line) network. In this paper, we first empirically evaluate some statistical models against the degree distribution of the country's call graph and determine that a Pareto log-normal distribution provides the best fit, despite claims in the literature that power-law distribution is the best model. We then question how network operator, size, density, and location affect degree distribution to understand the parameters governing it in social networks. Our empirical analysis indicates that changes in density, operator and location do not show a particular correlation with degree distribution; however, the average degree of social networks is proportional to the logarithm of network size. We also report on the structural properties of the communication network. These novel results are useful for managing and planning communication networks. INDEX TERMS Social networks, degree analysis, call graph, empirical analysis, tera-scale dataset

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Modeling Epidemics Spreading on Social Contact Networks

Cited by 74 publications

References 36 publications

Efficient Algorithm for Maximal Clique Size Evaluation

Efficient Algorithm for Maximal Clique Size Evaluation

Modelling Spreading Process Induced by Agent Mobility in Complex Networks

An Analysis of Social Networks Based on Tera-Scale Telecommunication Datasets

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