Identifying influential nodes in complex networks

Chen, Duanbing; Lü, Linyuan; Shang, Mingsheng; Zhang, Yicheng; Zhou, Tao

doi:10.1016/j.physa.2011.09.017

Cited by 1,019 publications

(540 citation statements)

References 41 publications

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“…Classical centrality measures, such as degree, closeness centrality, betweenness centrality and eigenvector centrality, are direct methods for recognizing the influential spreaders. However, closeness centrality and betweenness centrality have very high computational complexity, hence, it is not suitable to be applied into very largescale OSNs [18,19]. This limitation has made impractical for large OSNs.…”

Section: Related Workmentioning

confidence: 99%

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

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Abstract. Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

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Section: Related Workmentioning

confidence: 99%

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

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“…Based on these basic measures, researchers have proposed many other influence-mining algorithms [5][6][7][8][9][10][11][12][13][14]. Degree centrality is a simple metric and has low computational complexity; however, its result is not sufficiently accurate because it considers only the local node information.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Influential Nodes in Social Networks by Local Centrality with a Coefficient

Zhao

Fang-ai

Wang

et al. 2017

IJGI

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Influential nodes are rare in social networks, but their influence can quickly spread to most nodes in the network. Identifying influential nodes allows us to better control epidemic outbreaks, accelerate information propagation, conduct successful e-commerce advertisements, and so on. Classic methods for ranking influential nodes have limitations because they ignore the impact of the topology of neighbor nodes on a node. To solve this problem, we propose a novel measure based on local centrality with a coefficient. The proposed algorithm considers both the topological connections among neighbors and the number of neighbor nodes. First, we compute the number of neighbor nodes to identify nodes in cluster centers and those that exhibit the "bridge" property. Then, we construct a decreasing function for the local clustering coefficient of nodes, called the coefficient of local centrality, which ranks nodes that have the same number of four-layer neighbors. We perform experiments to measure node influence on both real and computer-generated networks using six measures: Degree Centrality, Betweenness Centrality, Closeness Centrality, K-Shell, Semi-local Centrality and our measure. The results show that the rankings obtained by the proposed measure are most similar to those of the benchmark Susceptible-Infected-Recovered model, thus verifying that our measure more accurately reflects the influence of nodes than do the other measures. Further, among the six tested measures, our method distinguishes node influence most effectively.

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Section: Introductionmentioning

confidence: 99%

“…Indexes such as degree centrality, betweenness centrality, and closeness centrality have an outstanding ability to identify key nodes from the network [11][12][13][14]. Sheikhahmadi et al [12] proposed an improved high degree centrality index to identify the S. Sun, H. Li, X. Xu: A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway most influential nodes, which outperformed other indexes on large-scale networks in terms of maximizing the spread of influence with acceptable running time.…”

Section: Introductionmentioning

confidence: 99%

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A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

Sun

2017

PROMET

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INTRODUCTIONWith the rapid development of urban rail transit network operation technique, the increase of passenger volume leads to congestion in stations [1], causing even safety risk in the network [2]. Besides, the topology of urban rail transit network can also aggravate or relief the congestion situation of subway networks. Therefore, an effective method identified the key station is an imperative to manage the overcrowded urban rail transit.For the factors of the station performance, many of the current approaches focus on the relationship between passenger flow and facility capacity. Xu et al.[3] combined queuing theory with facility utilization evaluation to conduct the station capacity evaluation, according to the gathering and scattering process of a station. Kepaptsoglou and Karlaftis [4] proposed a model to evaluate passenger evolution on subway station platforms, which usually gathered most passengers. Wang et al.[5] also used the passenger evacuation capacity to evaluate the station based on the facility utilization. If the service level of the facility is too low, there are big problems in the station.Besides, passengers' subjective feelings and other factors are also used for evaluation. Matulin et al.[6] proposed a two-level evaluation method to analyze public transport performances from micro and macro levels, using performance indicators and quality of service indicators. Kesten and Öğüt [7] developed a measurement framework of passenger-oriented performance to evaluate rail transportation, using satisfaction and importance ratings obtained from passenger surveys.Meanwhile, there are many approaches employed in the station evaluation. Some typical methods such as Data Envelopment Analysis (DEA) have played their great role in the station assessment. Sancha et al. [8] used the method to estimate the technical, social and environmental efficiency of transit transfer stations. Pjevčević et al. [9] used DEA method to measure and analyze the efficiencies of ports on the Danube River, which can be borrowed for station evaluation. In addition, simulation is also another method, where relevant simulation or operation data are provided. Zhang et al. [10] proposed a cellular automata-based model to simulate passengers' alighting and boarding behaviours in metro stations, aiming to analyze and evaluate the operation performance of stations. However, most works above do not consider the influence of other stations in the subway system, which results in local rather than global conclusions.Fortunately, in recent years, a complex network theory has been introduced to the station evaluation from a global viewpoint. Indexes such as degree centrality, betweenness centrality, and closeness centrality have an outstanding ability to identify key nodes from the network [11][12][13][14]. Sheikhahmadi et al. [12] proposed an improved high degree centrality index to identify the Traffic Management Original Scientific Paper

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Identifying influential nodes in complex networks

Cited by 1,019 publications

References 41 publications

Identifying the influential spreaders in multilayer interactions of online social networks

Identifying the influential spreaders in multilayer interactions of online social networks

Evaluating Influential Nodes in Social Networks by Local Centrality with a Coefficient

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

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