The Epidemic Spreading Processes in Complex Networks

2018 ◽  
pp. 69-98
Author(s):  
Chengyi Xia ◽  
Zhishuang Wang ◽  
Chunyun Zhen
Keyword(s):  
Complex Networks ◽  
Epidemic Spreading ◽  
Spreading Processes

scholarly journals An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1216
Author(s):  
Jedidiah Yanez-Sierra ◽  
Arturo Diaz-Perez ◽  
Victor Sosa-Sosa
Keyword(s):  
Complex Networks ◽  
Information Diffusion ◽  
Large Scale ◽  
Centrality Measures ◽  
Correct Identification ◽  
Graph Analysis ◽  
Relevant Role ◽  
Two Phases ◽  
Large Scale Networks ◽  
Spreading Processes

One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks.

scholarly journals Heterogeneous node responses to multi-type epidemics on networks

2020 ◽  
Vol 476 (2243) ◽  
pp. 20200587
Author(s):  
S. Moore ◽  
T. Rogers
Keyword(s):  
Network Topology ◽  
Message Passing ◽  
Contact Network ◽  
Epidemic Spreading ◽  
Infection Parameters ◽  
Infection Types ◽  
Heterogeneous Node ◽  
Spreading Processes

Having knowledge of the contact network over which an infection is spreading opens the possibility of making individualized predictions for the likelihood of different nodes to become infected. When multiple infective strains attempt to spread simultaneously we may further ask which strain, or strains, are most likely to infect a particular node. In this article we investigate the heterogeneity in likely outcomes for different nodes in two models of multi-type epidemic spreading processes. For models allowing co-infection we derive message-passing equations whose solution captures how the likelihood of a given node receiving a particular infection depends on both the position of the node in the network and the interaction between the infection types. For models of competing epidemics in which co-infection is impossible, a more complicated analysis leads to the simpler result that node vulnerability factorizes into a contribution from the network topology and a contribution from the infection parameters.

scholarly journals Control of epidemic spreading on complex networks by local traffic dynamics

2011 ◽  
Vol 84 (4) ◽  
Author(s):  
Han-Xin Yang ◽  
Wen-Xu Wang ◽  
Ying-Cheng Lai ◽  
Yan-Bo Xie ◽  
Bing-Hong Wang
Keyword(s):  
Complex Networks ◽  
Epidemic Spreading ◽  
Traffic Dynamics

scholarly journals Fundamentals of spreading processes in single and multilayer complex networks

2018 ◽  
Vol 756 ◽  
pp. 1-59 ◽  
Author(s):  
Guilherme Ferraz de Arruda ◽  
Francisco A. Rodrigues ◽  
Yamir Moreno
Keyword(s):  
Complex Networks ◽  
Spreading Processes

scholarly journals Path-integral formulation of spreading processes in complex networks

2021 ◽  
Author(s):  
Flavio Iannelli ◽  
Igor M. Sokolov
Keyword(s):  
Complex Networks ◽  
Path Integral ◽  
Disease Transmission ◽  
Arrival Time ◽  
Geodesic Distance ◽  
Integral Formulation ◽  
Numerical Estimation ◽  
Path Integral Formulation ◽  
Spreading Processes

AbstractWe introduce a path-integral formulation of network-based measures that generalize the concept of geodesic distance and that provides fundamental insights into the dynamics of disease transmission as well as an efficient numerical estimation of the infection arrival time.

scholarly journals Modeling Epidemic Spreading in Complex Networks: Concurrency and Traffic

2011 ◽  
pp. 435-462 ◽  
Author(s):  
Sandro Meloni ◽  
Alex Arenas ◽  
Sergio Gómez ◽  
Javier Borge-Holthoefer ◽  
Yamir Moreno
Keyword(s):  
Complex Networks ◽  
Epidemic Spreading
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