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Information Spreading Dynamics in Complex Networks

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 336

Special Issue Editor


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Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: complex networks; temporal networks; dynamics; ranking

Special Issue Information

Dear Colleagues,

How can we quickly and effectively promote new commercial products and suppress the spread of rumors and infectious diseases? How can we mine influential spreaders or community structures in complex networks? How do industrial structures affect economic development? These are all issues that are closely related to social networks’ complex structure. Information dissemination in complex networks is an extremely important research topic, the dynamics of which are addressed in this Special Issue. The main topics include but are not limited to the following: 

  1. spreading process and phase transition in complex networks, especially in temporal networks;
  2. information spreading, network attack and defense, and network security in resilient and recoverable networks;
  3. influence spreaders or community structure detection; 
  4. collaborative evolution and mutual influence between information spreading and network structure, especially between spreading and high-order network structure.

Prof. Dr. Duanbing Chen
Guest Editor

Manuscript Submission Information

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Keywords

  • information spreading
  • complex networks
  • temporal networks
  • resilient and recoverable networks
  • simplicial complex
  • influential spreaders
  • phase transition
  • network attack and defense
  • collaborative evolution

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Published Papers (1 paper)

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Research

13 pages, 6378 KiB  
Article
Epidemic Dynamics and Intervention Measures in Campus Settings Based on Multilayer Temporal Networks
by Xianyang Zhang and Ming Tang
Entropy 2025, 27(5), 543; https://doi.org/10.3390/e27050543 - 21 May 2025
Viewed by 23
Abstract
This study simulates the spread of epidemics on university campuses using a multilayer temporal network model combined with the SEIR (Susceptible–Exposed–Infectious–Recovered) transmission model. The proposed approach explicitly captures the time-varying contact patterns across four distinct layers (Rest, Dining, Activity, and Academic) to reflect [...] Read more.
This study simulates the spread of epidemics on university campuses using a multilayer temporal network model combined with the SEIR (Susceptible–Exposed–Infectious–Recovered) transmission model. The proposed approach explicitly captures the time-varying contact patterns across four distinct layers (Rest, Dining, Activity, and Academic) to reflect realistic student mobility driven by class schedules and spatial constraints. It evaluates the impact of various intervention measures on epidemic spreading, including subnetwork closure and zoned management. Our analysis reveals that the Academic and Activity layers emerge as high-risk transmission hubs due to their dynamic, high-density contact structures. Intervention measures exhibit layer-dependent efficacy: zoned management is highly effective in high-contact subnetworks, its impact on low-contact subnetworks remains limited. Consequently, intervention measures must be dynamically adjusted based on the characteristics of each subnetwork and the epidemic situations, with higher participation rates enhancing the effectiveness of these measures. This work advances methodological innovation in temporal network epidemiology by bridging structural dynamics with SEIR processes, offering actionable insights for campus-level pandemic preparedness. The findings underscore the necessity of layer-aware policies to optimize resource allocation in complex, time-dependent contact systems. Full article
(This article belongs to the Special Issue Information Spreading Dynamics in Complex Networks)
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