scholarly journals Rejection-Based Simulation of Stochastic Spreading Processes on Complex Networks

2019 ◽  
pp. 63-79 ◽  
Author(s):  
Gerrit Großmann ◽  
Verena Wolf
Keyword(s):  
Complex Networks ◽  
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 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 Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm

2019 ◽  
Vol 240 ◽  
pp. 30-37 ◽  
Author(s):  
Guillaume St-Onge ◽  
Jean-Gabriel Young ◽  
Laurent Hébert-Dufresne ◽  
Louis J. Dubé
Keyword(s):  
Complex Networks ◽  
Rejection Algorithm ◽  
Efficient Sampling ◽  
Spreading Processes

scholarly journals Parametrization of Spreading Processes Within Complex Networks with the Use of Knowledge Acquired from Network Samples

2019 ◽  
Vol 159 ◽  
pp. 2279-2293 ◽  
Author(s):  
Artur Karczmarczyk ◽  
Jaroslaw Jankowski ◽  
Jaroslaw Watrobski
Keyword(s):  
Complex Networks ◽  
Use Of Knowledge ◽  
Spreading Processes

Generalized Epidemic Mean-Field Model for Spreading Processes Over Multilayer Complex Networks

2013 ◽  
Vol 21 (5) ◽  
pp. 1609-1620 ◽  
Author(s):  
Faryad Darabi Sahneh ◽  
Caterina Scoglio ◽  
Piet Van Mieghem
Keyword(s):  
Complex Networks ◽  
Field Model ◽  
Mean Field ◽  
Mean Field Model ◽  
Spreading Processes
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