Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi, Mohammed Ali; Varathan, Kasturi Dewi; Ravana, Sri Devi; Ahmed, Ejaz; Chang, Victor

doi:10.3233/jifs-169112

Cited by 34 publications

(21 citation statements)

References 65 publications

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“…In the area of time-varying networks, most of these networks are constantly changing, which poses the challenge of identifying influential spreaders since they could shift with the changing topology. In the way of multilayer networks, it contains information from different dimensions with interaction between layers and has attracted lots of research interest [ 101 , 102 , 103 ]. To identify influential nodes in multilayer networks, we need to further consider the method to better combine information from different layers and relations between them.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Influential Nodes Identification in Complex Networks via Information Entropy

Guo

Yang

Chen³

et al. 2020

Entropy

View full text Add to dashboard Cite

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“…One observation was parasitic amplification owing to host-parasite and predator-prey interactions, which is related to our study of synergy. Similarly, synergistic diffusion may be analyzed in online social networks, by considering visibility and media relatedness [38] and topology [39]. Additionally, investigating the shift in sensitivity using lag-regression would yield quantitative insight regarding the precise relationship between dormancy and topology-specific diffusion rate, using techniques as described environmental epidemiology [40].…”

Section: Discussionmentioning

confidence: 99%

Co-contagion diffusion on multilayer networks

Chang

2019

Appl Netw Sci

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This study examines the interface of three elements during co-contagion diffusion: the synergy between contagions, the dormancy rate of each individual contagion, and the multiplex network topology. Dormancy is defined as a weaker form of "immunity," where dormant nodes no longer actively participate in diffusion, but are still susceptible to infection. The proposed model extends the literature on threshold models, and demonstrates intricate interdependencies between different graph structures. Our simulations show that first, the faster contagion induces branching on the slower contagion; second, shorter characteristic path lengths diminish the impact of dormancy in lowering diffusion. Third, when two long-range graphs are paired, the faster contagion depends on both dormancy rates, whereas the slower contagion depends only on its own; fourth, synergistic contagions are less sensitive to dormancy, and have a wider window to diffuse. Furthermore, when long-range and spatially constrained graphs are paired, ring vaccination occurs on the spatial graph and produces partial diffusion, due to dormant, surrounding nodes. The spatial contagion depends on both dormancy rates whereas the long-range contagion depends on only its own.

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“…In [37], the authors investigated diffusion issues with an improved version of the K-core method [83]. The authors incorporate a linking and weighting method based on the observation that users' interactions, namely retweets and mentions, are significant factors for quantifying their spreading capability in a network.…”

Section: Diffusion-oriented Approachesmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content. In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

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Identifying the influential spreaders in multilayer interactions of online social networks

Cited by 34 publications

References 65 publications

Influential Nodes Identification in Complex Networks via Information Entropy

Influential Nodes Identification in Complex Networks via Information Entropy

Co-contagion diffusion on multilayer networks

Modeling Influence with Semantics in Social Networks

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