Signal propagation in complex networks

Ji, Peng; Ye, Jiachen; Mu, Yu; Lin, Wei; Tian, Yang; Hens, Chittaranjan; Perc, Matjaž; Tang, Yang; Sun, Jie; Kurths, Jürgen

doi:10.1016/j.physrep.2023.03.005

Cited by 104 publications

(14 citation statements)

References 844 publications

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“…Mathematical models have been developed to understand complex network structures in fields such as epidemiology, social dynamics, signaling networks, and neuroscience 10 . In particular, disease transmission has been investigated 11 .…”

Section: Introductionmentioning

confidence: 99%

“…9 Mathematical models have been developed to understand complex network structures in fields such as epidemiology, social dynamics, signaling networks, and neuroscience. 10 In particular, disease transmission has been investigated. 11 For example, to overcome the recent coronavirus disease 2019 pandemic, several mathematical model-based effective strategies were suggested.…”

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confidence: 99%

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Effective vaccination strategies for human papillomavirus (HPV) infection and cervical cancer based on the mathematical model with a stochastic process

Kim,

Kim

2024

Journal of Medical Virology

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Human papillomavirus (HPV) infection poses a significant risk to women's health by causing cervical cancer. In addition to HPV, cervical cancer incidence rates can be influenced by various factors, including human immunodeficiency virus and herpes, as well as screening policy. In this study, a mathematical model with stochastic processes was developed to analyze HPV transmission between genders and its subsequent impact on cervical cancer incidence. The model simulations suggest that both‐gender vaccination is far more effective than female‐only vaccination in preventing an increase in cervical cancer incidence. With increasing stochasticity, the difference between the number of patients in the vaccinated group and the number in the nonvaccinated group diminishes. To distinguish the patient population distribution of the vaccinated from the nonvaccinated, we calculated effect size (Cohen's distance) in addition to Student's t‐test. The model analysis suggests a threshold vaccination rate for both genders for a clear reduction of cancer incidence when significant stochastic factors are present.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Effective vaccination strategies for human papillomavirus (HPV) infection and cervical cancer based on the mathematical model with a stochastic process

Kim,

Kim

2024

Journal of Medical Virology

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“…Synchronization is a collective process in which a coupled population, under certain conditions, becomes self-organized in such a way that their components evolve to follow the same dynamical pattern [1][2][3][4]. This process is one of the most attractive phenomena in nature and some common examples are the synchronization of flashing fireflies [2,5], a crowd clapping in unison [6], synchronization in arrays of Josephson junctions [7], among many others [2,3,8].…”

Section: Introductionmentioning

confidence: 99%

“…Although its simplicity, the Kuramoto model provides a phenomenological description of the problem displaying rich emergent dynamics such as the phase transition from incoherence to synchrony [20][21][22], and provides insight into synchronization process in nature [23][24][25][26]. The Kuramoto model has been widely studied and its variations include the presence of noise [27,28], inertia [29][30][31], weighted coupling [32][33][34], time delay [35,36], resetting [37][38][39], among many others [4,21,22,40,41].…”

Section: Introductionmentioning

confidence: 99%

Influence of cumulative damage on synchronization of Kuramoto oscillators on networks

Eraso-Hernandez,

Riascos

2023

J. Phys. A: Math. Theor.

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In this paper, we study the synchronization of identical Kuramoto phase oscillators under cumulative stochastic damage to the edges of networks. We analyze the capacity of coupled oscillators to reach a coherent state from initial random phases. The process of synchronization is a global function performed by a system that gradually changes when the damage weakens individual connections of the network. We explore diverse structures characterized by different topologies. Among these are deterministic networks as a wheel or the lattice formed by the movements of the knight on a chess board, and random networks generated with the Erd\H{o}s-Rényi and Barab\'asi-Albert algorithms. In addition, we study the synchronization times of 109 non-isomorphic graphs with six nodes. The synchronization times and other introduced quantities are sensitive to the impact of damage, allowing us to measure the reduction of the capacity of synchronization and classify the effect of damage in the systems under study. This approach is general and paves the way for the exploration of the effect of damage accumulation in diverse dynamical processes in complex systems.

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“…It can reveal the generation and evolution mechanism of a network structure. On the other hand, it has a wide range of applications, such as recommending friends in online social networks, identifying interacting genetic pairs in genetic networks, and analyzing signal propagation in complex networks [13], etc.…”

Section: Introductionmentioning

confidence: 99%

LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

2023

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The individuals of real-world networks participate in various types of connections, each forming a layer in multiplex networks. Link prediction is an important problem in multiplex network analysis owing to its wide range of practical applications, such as mining drug targets, recommending friends in social networks, and exploring network evolution mechanisms. A key issue of link prediction within multiplex networks is how to estimate the likelihood of potential links in the predicted layer by leveraging both interlayer and intralayer information. Several studies have shown that incorporating interlayer topological information can improve the performance of link prediction in the predicted layer. Therefore, this paper proposes the Link Prediction based on Global Relevance of Interlayer (LPGRI) method to estimate the likelihood of potential links in the predicted layer of multiplex networks, which comprehensively utilizes both types of information. In the LPGRI method, the contribution of interlayer information is determined using the global relevance (GR) index between layers. Experimental studies on six real multiplex networks demonstrate the competitive performance of our method.

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Signal propagation in complex networks

Cited by 104 publications

References 844 publications

Effective vaccination strategies for human papillomavirus (HPV) infection and cervical cancer based on the mathematical model with a stochastic process

Effective vaccination strategies for human papillomavirus (HPV) infection and cervical cancer based on the mathematical model with a stochastic process

Influence of cumulative damage on synchronization of Kuramoto oscillators on networks

LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

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