Revealing the predictability of intrinsic structure in complex networks

Sun, Jiachen; Feng, Ling; Xie, Jiarong; Ma, Xiao; Wang, Dashun; Hu, Yanqing

doi:10.1038/s41467-020-14418-6

Cited by 21 publications

(22 citation statements)

References 36 publications

(79 reference statements)

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“…(2020) suggested that the structural regularity corresponds to redundant information in the adjacency matrix, which can be characterized by a low-rank and sparse representation matrix. Sun et al. (2020) proposed a more direct method to measure such redundancy.…”

Section: Link Predictabilitymentioning

confidence: 99%

Progresses and challenges in link prediction

Zhou

2021

iScience

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Summary Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.

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Section: Link Predictabilitymentioning

confidence: 99%

Progresses and challenges in link prediction

Zhou

2021

iScience

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“…Secondly, a two-layer coupling network is built for each subpopulation to portray the coevolution of epidemic and awareness within each city (Figure 1(b)), where nodes represent individuals [17,27,28]. e virtual contact layer describes the awareness diffusion based on the UAU model, and each node in this layer has two possible states: Unaware (U), representing that the individual has no self-protection awareness due to objective or subjective reasons, and Aware (A), referring to individuals with awareness of protecting themselves from this disease [29].…”

Section: 2mentioning

confidence: 99%

Understanding the Anticontagion Process and Reopening of China during COVID-19 via Coevolution Network of Epidemic and Awareness

Fan

Zeng

et al. 2021

Complexity

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The novel coronavirus (COVID-19) pandemic is intensifying all over the world, but some countries, including China, have developed extensive and successful experience in controlling this pandemic. In this context, some questions arise naturally: What can countries caught up in the epidemic learn from China’s experience? In regions where the outbreak is under control, what would lead to a resurgence of the epidemic? To address these issues, we investigate China’s experience in anticontagion interventions and reopening process, focusing on the coevolution of epidemic and awareness during COVID-19 outbreak. Through an empirical analysis based on large-scale data and simulation based on a metapopulation and multilayer network model, we ascertain the impact of human movements and awareness diffusion on the epidemic, elucidate the inherent patterns and effective interventions of different epidemic prevention methods, and highlight the crunch time of each measure. The results are also employed to analyze COVID-19 evolution in other countries so as to find unified rules in complex situations around the world and provide advice on anticontagion and reopening policies. Our findings explain some key mechanisms of epidemic prevention and may help the epidemic analysis and decision-making in various countries suffering from COVID-19.

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“…Although many works based on contact network models have been done on the resource allocation for epidemic control, most of them assume that contacts are static through time. However, contact patterns are not static [28][29][30][31] and abundant close contacts in heterogeneous social networks promote both epidemic outbreak and spreading [32][33][34][35]. In recent years, the activity-driven (AD) temporal network model has attracted a large amount of attention for considering the dynamic connectivity patterns in real networks [19,[36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time-Varying Multiplex Networks

et al. 2021

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In the real world, individual resources are crucial for patients when epidemics outbreak. Thus, the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks. However, the social relationships of individuals are time-varying, which affects such coupled dynamics. For that, we propose a coupled resource-epidemic (RNR-SIS) dynamic model (coupled model for short) on a time-varying multiplex network to synchronously simulate the resource diffusion and epidemic spreading in dynamic social networks. The equilibrium analysis of the coupled model is conducted in a general scenario where the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states. By using the microscopic Markov chain approach and Monte Carlo simulations, we determine a probabilistic framework of the intralayer and interlayer dynamic processes of the coupled model and obtain the outbreak threshold of epidemic spreading. Meanwhile, the experimental results show the trivially asymmetric interactions between resource diffusion and epidemic spreading. They also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion, effectively suppressing epidemic spreading. However, these two individual characters in the epidemic layer can cause more resource depletion, which greatly promotes epidemic spreading. Furthermore, we also find that the contact capacity finitely impacts the coupled dynamics of resource diffusion and epidemic spreading.

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Revealing the predictability of intrinsic structure in complex networks

Cited by 21 publications

References 36 publications

Progresses and challenges in link prediction

Progresses and challenges in link prediction

Understanding the Anticontagion Process and Reopening of China during COVID-19 via Coevolution Network of Epidemic and Awareness

Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time-Varying Multiplex Networks

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