Identifying influential spreaders by gravity model

Li, Zhe; Ren, Tao; Ma, Xiaoqi; Liu, Simiao; Zhang, Yixin; Zhou, Tao

doi:10.1038/s41598-019-44930-9

Cited by 122 publications

(75 citation statements)

References 43 publications

(50 reference statements)

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“…However, they are also with high computational complexity by globally calculating k -shell. In 2019, Li et al improved the gravity centrality and proposed a Local-Gravity centrality (LGR) [ 33 ] by replacing k -shell computing and merely considering the neighbors within R steps, defined as

where

and

are the degrees of i and j , respectively,

is the shortest path length between i and j . This method had been extremely successful in a variety of real-world datasets, however, the parameter R requires the calculating of network diameter, which is also a time-consuming process.…”

Section: Related Workmentioning

confidence: 99%

Identifying Influencers in Social Networks

Huang

Chen

Wang

et al. 2020

Entropy

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Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

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where

and

are the degrees of i and j , respectively,

Section: Related Workmentioning

confidence: 99%

Identifying Influencers in Social Networks

Huang

Chen

Wang

et al. 2020

Entropy

Self Cite

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“…where R is the truncation radius. Reference [17] reveals that the optimal truncation radius, denoted by R * , approximately scales linearly with the average distance, denoted by d , as…”

Section: Related Workmentioning

confidence: 99%

“…Recently, some more potential methods that only use the semi-local structural information are proposed and perform much better than the above well-known state-of-the-art methods, such as Quasi-Laplacian centrality [16] (QC) and Local Gravity Model [17] (LGM). QC is defined as the drop of the Laplacian energy of the network with the deletion of the target node from the network.…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Spreaders Based on Adaptive Weighted Link Model

Ren

et al. 2020

IEEE Access

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Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively. The adaptive weighted link model is completely depends on the semi-local topological structure and thus can be calculated not only faster but also under the case where the global topology is not known, especially when the network is sparse, the time complexity is approximate linear. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks show that the adaptive weighted link model always perform the best in comparison with well-known state-of-the-art methods. INDEX TERMS Complex networks, influential spreaders, semi-local information, adaptive method.

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“…The gravity we focus on in this paper is utilized to describe the strength of the interactions between the two nodes. The gravity in networks [46] is defined as…”

Section: Definitionsmentioning

confidence: 99%

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

2020

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Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

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Identifying influential spreaders by gravity model

Cited by 122 publications

References 43 publications

Identifying Influencers in Social Networks

Identifying Influencers in Social Networks

Identifying Influential Spreaders Based on Adaptive Weighted Link Model

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

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