Effective spreading from multiple leaders identified by percolation in the susceptible-infected-recovered (SIR) model

Ji, Shenggong; Lü, Linyuan; Yeung, Chi Ho; Hu, Yanqing

doi:10.1088/1367-2630/aa76b0

Cited by 24 publications

(14 citation statements)

References 36 publications

(52 reference statements)

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“…Considering this natural relation, Ji et al [266] proposed a bond percolation-based method to identify the optimal combination of given number of influential spreaders. To find W influential nodes, given the probability p, each edge will be removed with probability p and then m isolated clusters will appear after the link removal.…”

Section: Percolation Methodsmentioning

confidence: 99%

“…Then the ratio of final recovered nodes is used to measure the algorithms' performance. We test seven methods including degree, betweenness, closeness, Ji's percolation method [266], Hu's percolation method [271], CI [188] and VoteRank [206]. For the two parameter dependent methods, we set L = 0.05n for Ji's method and l = 2 for CI.…”

Section: Finding a Set Of Vital Nodesmentioning

confidence: 99%

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Vital nodes identification in complex networks

et al. 2016

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Real networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify vital nodes is thus very significant, allowing us to control the outbreak of epidemics, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The vital nodes identification attracts increasing attentions from both computer science and physical societies, with algorithms ranging from simply counting the immediate neighbors to complicated machine learning and message passing approaches. In this review, we clarify the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art. Furthermore, we provide extensive empirical analyses to compare well-known methods on disparate real networks, and highlight the future directions. In despite of the emphasis on physics-rooted approaches, the unification of the language and comparison with cross-domain methods would trigger interdisciplinary solutions in the near future.

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Section: Percolation Methodsmentioning

confidence: 99%

Section: Finding a Set Of Vital Nodesmentioning

confidence: 99%

Vital nodes identification in complex networks

et al. 2016

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“…Our understanding of percolation properties of networks has expanded significantly in recent years. Percolation theory, a classical model in statistical physics, has been applied in a number of different network science topics, such as network structure [ 1 , 2 ], network robustness [ 3–5 ], node ranking [ 6 ] and community detection [ 7 ], as well as in studies of network dynamics, such as information spreading [ 8 ], and highway traffic flows [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

History-dependent percolation on multiplex networks

Lü

Deng

et al. 2020

National Science Review

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The structure of interconnected systems and its impact on the system dynamics is a much-studied crossdisciplinary topic. Although various critical phenomena have been found in different models, the study on the connections between different percolation transitions is still lacking. Here we propose a unified framework to study the origins of the discontinuous transitions of the percolation process on interacting networks. The model evolves in generations with the result of the present percolation depending on the previous state and thus is history-dependent. Both theoretical analysis and Monte Carlo simulations reveal that the nature of the transition remains the same at finite generations but exhibits an abrupt change for the infinite generation. We use brain functional correlation and morphological similarity data to show that our model also provides a general method to explore the network structure and can contribute to many practical applications, such as detecting the abnormal structures of human brain networks.

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“…Ji el al. [ 67 ] considered percolation in the network to obtain a set of distributed and coordinated spreaders. Researchers also seek to maximize the influence by studying communities [ 68 , 69 , 70 , 71 , 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

Influential Nodes Identification in Complex Networks via Information Entropy

Guo

Yang

Chen³

et al. 2020

Entropy

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Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes' spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.Entropy 2020, 22, 242 2 of 19 diffusion [9], and even detect essential proteins [10]. On the other hand, by removing some critical nodes, it can greatly reduce the connectivity of the network to restrain the outbreak of epidemics [11] or spreading of rumors [12].The ongoing COVID-19 epidemics is catching wide attention around the world. Every country is making enormous effort to control the virus spreading. By analyzing social networks, it would be easier for us to control epidemics spreading. In the last decades, propagation dynamics has always been an important research direction. Many mechanisms, such as epidemic spreading [13][14][15][16], rumor propagation [17,18], social sudden events spreading [19], and e-commercial advertisements, are all closely related to complex network dynamics. Early in 1760, Daniel Bernoulli studied smallpox vaccine by using ordinary differential equations, and gave the Bernoulli equations [20] , which is one of the earliest propagation dynamics models. Later, Hamer presented the mass-action principle [21,22] when studying the recurring epidemics of measles. A.G. McKendrick and W.O. Kermack formulated a famous modern mathematical epidemic model named the Susceptible-Infected-Recovered (SIR) compartmental model when studying the spreading pattern of the Black Death and the plague in 1906. SIR compartmental model successfully predicted the outbreak of several epidemics [23]. Harding et al. [24] followed the maximum entropy (MaxEnt) principle when simulating on the SIS model to study epidemics spreading on networks. Wang et ...

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Effective spreading from multiple leaders identified by percolation in the susceptible-infected-recovered (SIR) model

Cited by 24 publications

References 36 publications

Vital nodes identification in complex networks

Vital nodes identification in complex networks

History-dependent percolation on multiplex networks

Influential Nodes Identification in Complex Networks via Information Entropy

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