Epidemic spreading on complex networks with overlapping and non-overlapping community structure

Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

doi:10.1016/j.physa.2014.10.023

Cited by 51 publications

(20 citation statements)

References 36 publications

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“…The occurrence of spatial or social community structures within a population result in a social network with high modularity (whereby intra‐group interactions predominate over inter‐group interactions; Newman, ; Sah, Méndez, & Bansal, ). Populations with more modular social networks typically experience smaller and slower‐spreading epidemics (Griffin & Nunn, ; Miller, ; Newman, ; Salathé & Jones, ; Shang, Liu, Li, Xie, & Wu, ). This reduction in disease spread occurs because high fragmentation and close‐knit subgroupings delay the spread of disease and serve to “trap” infections within networks (Sah, Leu, Cross, Hudson, & Bansal, ).…”

Section: Introductionmentioning

confidence: 99%

Social structure contains epidemics and regulates individual roles in disease transmission in a group‐living mammal

Rozins

Silk

Croft

et al. 2018

Ecology and Evolution

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Population structure is critical to infectious disease transmission. As a result, theoretical and empirical contact network models of infectious disease spread are increasingly providing valuable insights into wildlife epidemiology. Analyzing an exceptionally detailed dataset on contact structure within a high‐density population of European badgers Meles meles, we show that a modular contact network produced by spatially structured stable social groups, lead to smaller epidemics, particularly for infections with intermediate transmissibility. The key advance is that we identify considerable variation among individuals in their role in disease spread, with these new insights made possible by the detail in the badger dataset. Furthermore, the important impacts on epidemiology are found even though the modularity of the Badger network is much lower than the threshold that previous work suggested was necessary. These findings reveal the importance of stable social group structure for disease dynamics with important management implications for socially structured populations.

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Section: Introductionmentioning

confidence: 99%

Social structure contains epidemics and regulates individual roles in disease transmission in a group‐living mammal

Rozins

Silk

Croft

et al. 2018

Ecology and Evolution

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show abstract

“…By simulating epidemic models on both synthetic and real-world networks, they found that overlapping vertices play a vital role in the spread of epidemics across communities. Research [24] proposed a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. They experimented on the SIS epidemic process in the generated scale-free networks and real-world networks.…”

Section: Related Workmentioning

confidence: 99%

“…But their level of overlapping communities is fixed. Research [24] investigated the epidemic process on a scale-free network model with different levels of overlapping communities. They all illustrate the important role of overlapping communities in the process of epidemic propagation.…”

Section: Introductionmentioning

confidence: 99%

Information Propagation in Social Networks with Overlapping Community Structure

Zhao¹,

Liu²

2017

KSII TIIS

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Many real networks exhibit overlapping community structures. Recent studies have been performed that analyze the impact of overlapping community structure on information propagation, but few of them concerned with individual behaviors. From this point of view, we propose a Markov process model to evaluate the performance of information propagation in social networks with overlapping community structures. In addition, many individual social behaviors are combined in the model. For example, individuals may exhibit selfish behaviors, such as individual and social selfishness, and people may discard the information after they have used it. The accuracy of the model is verified by simulation. Furthermore, the numerical results show that both overlapping community structure of the network and individual behaviors have a significant impact on the outbreak size and propagation speed of the information. Additionally, the overlapping community structure of the social network can reduce the impact of selfishness on information propagation.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Murilo S. Baptista. also has wide applications in real-world such as recommendation systems [1], link prediction [2], epidemic spreading modelling on networks [3], detection of terrorist groups from networks [4] etc.…”

Section: Introductionmentioning

confidence: 99%

A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks

Liu

Wang

2020

IEEE Access

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Community detection is a key aspect for understanding network structures and uncovers the underlying functions or characteristics of complex systems. A community usually refers to a set of nodes that are densely connected among themselves, but sparsely connected to the remaining nodes of the network. Detecting communities has been proved to be a NP-hard problem. Therefore, evolutionary based optimization approaches can be used to solve it. But a primary challenge for them is the higher computational complexity when dealing with large scale networks. In this respect, a COMpression based Multi-Objective Evolutionary Algorithm with Decomposition (Com-MOEA/D) for community detection is proposed where the network is first compressed to a much more smaller scale by exploring network topologies. After that, a framework of multi-objective evolutionary algorithm based on decomposition is applied, in which a local information based genetic operator is proposed to speed up the convergence and improve the accuracy of the Com-MOEA/D algorithm. Experimental results on both real world and synthetic networks show the superiority of the proposed method over several state-of-the-art community detection algorithms.

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Epidemic spreading on complex networks with overlapping and non-overlapping community structure

Cited by 51 publications

References 36 publications

Social structure contains epidemics and regulates individual roles in disease transmission in a group‐living mammal

Social structure contains epidemics and regulates individual roles in disease transmission in a group‐living mammal

Information Propagation in Social Networks with Overlapping Community Structure

A Compression-Based Multi-Objective Evolutionary Algorithm for Community Detection in Social Networks

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