A Novel Centrality of Influential Nodes Identification in Complex Networks

Yang, Young-Hoon; Wang, Xing; Chen, You; Hu, Min; Ruan, Cheng-wei

doi:10.1109/access.2020.2983053

Cited by 43 publications

(18 citation statements)

References 47 publications

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“…Yang et al [2], presented a novel mixed centrality measure which is consider as degree and clustering coefficient (DCC).DCC is defines based on four parts: effect of degree, neighbor's degree, clustering coefficient, and next level neighbor's clustering coefficient. The advantage of this mixed centrality measure is less time complexity which is O (nk 2 ) where n is number of node in the network and k is average degree.…”

Section: Mixed Centrality Measuresmentioning

confidence: 99%

A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

2021

IJATCSE

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Influential nodes refer to the ability of a node to spread information in complex networks. Identifying influential nodes is an important problem in complex networks which plays a key role in many applications such as rumor controlling, virus spreading, viral market advertising, research paper views, and citations. Basic measures like degree centrality, betweenness centrality, closeness centrality are identifying influential nodes but they are incapable of largescale networks due to time complexity issues. Chen et al. [1] proposed semi-local centrality, which is reducing computation complexity and finding influential nodes in the network. Recently Yang et al. 2020 [2] proposed a novel centrality measure based on degree and clustering coefficient for identifying the influential nodes. Sanjay et al. 2020 [3] gave voterank and neighborhood coreness-based algorithms for finding the influenced nodes in the network. Zhiwei et al. 2019 [4] considered the average shortest path to discover the influenced node in the network. These are the few recent local,global and mixed centralities. In this paper, we show a broad view of recent methods for finding influential nodes in complex networks. It also analyzes the new challenges and limitations for a better understanding of each method in detail. The experimental results based on these methods show better performance compared with existing basic centrality measures.

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Section: Mixed Centrality Measuresmentioning

confidence: 99%

A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

2021

IJATCSE

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“…Chen et al [33] claim that influential nodes can be identified by extracting and synthesizing topology feature information of traditional centrality indices and spreading influence. Yang et al [34] identify the influential nodes by incorporating the degree and clustering-coefficient of neighbor nodes. Berahmand et al [35] incorporate the natural characteristics of complex networks to capture the spreader node.…”

Section: B Network Centralitymentioning

confidence: 99%

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

2020

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Constructing social networks for real epidemic cases is very challenging. Many mathematical models have been proposed to model such networks using simulation models, such as susceptibleinfected (SI), susceptible-infected-recovered (SIR), and susceptible-exposed-infectious-removed (SEIR). Nonetheless, social network analyses can fail to capture real conditions, as such models are constructed based on many assumptions. Furthermore, unlike standard online social networks (OSNs), a social epidemic network requires different treatment from both model construction and social network analysis perspectives, especially for the detection of superspreaders. To address these issues, we propose a trajectory linkage method to automatically discover social networks from historical patient-trajectory data, wherein relations among patients are determined by spatial proximity and time-windows. Moreover, we introduce a novel spreader centrality measure that is devised to identify superspreaders in a social epidemic network. Extensive experiments were performed using real epidemic data. The results revealed that trajectory linkage can obtain a denser social network model than is possible by only incorporating patient data (the "who is infected by whom" relationship). By performing a social network analysis, the trajectory linkage model can express the real conditions of the patient relationship. Furthermore, our spreader centrality can capture the real superspreaders more effectively than can the existing centrality measure in social epidemic networks. INDEX TERMS Social networks, epidemic networks, network centrality, spreader centrality, social network analysis.

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“…The spread of a virus outbreak (such as Covid-19) can be estimated and precautions can be taken based on this [8]. By modeling the spread of gossip on the social network, the spread can be prevented [9], [10]. Or, the desired information may reach the maximum number of people [11].…”

Section: Introductionmentioning

confidence: 99%

Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model

Şimşek

2022

Journal of King Saud University - Computer and Information Scie

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A Novel Centrality of Influential Nodes Identification in Complex Networks

Cited by 43 publications

References 47 publications

A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model

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