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Algorithms
Special Issues
Advances in Complex Network Models and Random Graphs
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Advances in Complex Network Models and Random Graphs

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 2753

Special Issue Editors

Dr. Yilun Shang

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne, UK
Interests: mathematics; complex systems; networks; computer science; physics
Special Issues, Collections and Topics in MDPI journals
Dr. Yang (Felix) Lou

E-Mail Website
Guest Editor
Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Interests: complex networks; evolutionary computation; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many real-world systems can be modeled as complex networks, which have gained growing recognition and popularity in the last two decades, now becoming a self-contained discipline encompassing computer science, systems engineering, biological science, statistical physics, applied mathematics, social sciences and epidemiology.

We invite you to submit your latest research in the area of complex networks and related algorithms to this Special Issue, “Advances in Complex Network Models and Random Graphs”. We are looking for new and innovative complex network models and theories to analyse the real-world networked systems; as well efficient algorithms to solve and optimize the problems encounted with respect to these networked systems. High-quality papers are solicited to address both theoretical and practical issues of complex networks. Potential topics include, but are not limited to, physical and functional properties of complex networks, mathematical properties of complex network models and random graphs, nonlinear dynamical systems and collective behaviours in complex systems, network controllability and robustness enhancament and optimization, epidemiological study using complex networks, etc.

Keywords

  • Applications of Graph Theory
  • Community detection
  • Complex Networks
  • Controllability
  • Consensus
  • Epidemiology
  • Malicious attacks
  • Network dynamics
  • Optimization
  • Random Graphs
  • Robustness
  • Searching in networks

Published Papers (1 paper)

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Research

8 pages, 860 KiB  
Article
Dynamical Recovery of Complex Networks under a Localised Attack
by Fan Wang, Gaogao Dong and Lixin Tian
Algorithms 2021, 14(9), 274; https://doi.org/10.3390/a14090274 - 21 Sep 2021
Cited by 2 | Viewed by 1883
Abstract
In real systems, some damaged nodes can spontaneously become active again when recovered from themselves or their active neighbours. However, the spontaneous dynamical recovery of complex networks that suffer a local failure has not yet been taken into consideration. To model this recovery [...] Read more.
In real systems, some damaged nodes can spontaneously become active again when recovered from themselves or their active neighbours. However, the spontaneous dynamical recovery of complex networks that suffer a local failure has not yet been taken into consideration. To model this recovery process, we develop a framework to study the resilience behaviours of the network under a localised attack (LA). Since the nodes’ state within the network affects the subsequent dynamic evolution, we study the dynamic behaviours of local failure propagation and node recoveries based on this memory characteristic. It can be found that the fraction of active nodes switches back and forth between high network activity and low network activity, which leads to the spontaneous emergence of phase-flipping phenomena. These behaviours can be found in a random regular network, Erdős-Rényi network and Scale-free network, which shows that these three types of networks have the same or different resilience behaviours under an LA and random attack. These results will be helpful for studying the spontaneous recovery real systems under an LA. Our work provides insight into understanding the recovery process and a protection strategy of various complex systems from the perspective of damaged memory. Full article
(This article belongs to the Special Issue Advances in Complex Network Models and Random Graphs)
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