Identifying influential spreaders by weighted LeaderRank

Li, Qian; Zhou, Tao; Lü, Linyuan; Chen, Duanbing

doi:10.1016/j.physa.2014.02.041

Cited by 224 publications

(145 citation statements)

References 37 publications

(42 reference statements)

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“…Martin et al [30] made the point that the eigenvector centrality has lost the capacity to distinguish among the remaining nodes and introduced an alternative centrality definition called nonbacktracking centrality. The LeaderRank algorithm was modified by introducing a variant based on allocating degree-dependent weights onto associations constructed by ground nodes [31]. Zhao et al [32] identified the most effective spreaders by using community-based theory.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be "vital" to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary's karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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“…As the link relationship between webpages can be explained as the correlation and support between webpages, so too can the importance of the webpage be judged. Typical methods include the Hypertext-Induced Topic Search (HITS) algorithm [25] proposed by Kleinberg, the PageRank algorithm [26] used by Google and LeaderRank [27] proposed recently by Lv Linyuan et al Then in 2014, Weighted LeaderRank [28] as an improvement method was presented by Li et al Current research on identifying influential spreaders, many interesting conclusions were successively put forward, such as the role of clustering [29] by Chen D-B et al who also proposed to improve the identification of influential spreaders by the path diversity [30].…”

Section: Introductionmentioning

confidence: 99%

A Community-Based Approach to Identifying Influential Spreaders

Zhao

Wang

Zhang

et al. 2015

Entropy

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Identifying influential spreaders in complex networks has a significant impact on understanding and control of spreading process in networks. In this paper, we introduce a new centrality index to identify influential spreaders in a network based on the community structure of the network. The community-based centrality (CbC) considers both the number and sizes of communities that are directly linked by a node. We discuss correlations between CbC and other classical centrality indices. Based on simulations of the single source of infection with the Susceptible-Infected-Recovered (SIR) model, we find that CbC can help to identify some critical influential nodes that other indices cannot find. We also investigate the stability of CbC.

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“…As consequence eigenvector, centrality may lead to improper ranking if applied to such networks. Various studies have used PageRank and its extensions to identify the influential spreaders in OSNs [22][23][24][25][26][27]. Kitsak et al found, in contrast to common belief, there are plausible circumstances where the best spreaders do not correspond to the most highly connected or the most central people [28].…”

Section: Related Workmentioning

confidence: 99%

“…The most prominent ones include classical centrality measures in complex networks such as degree centrality [40][41][42], betweenness centrality [43], closeness centrality [44], and eigenvector centrality [45][46][47] , PageRank [22][23][24]48] and it extensions and k-core algorithm [28,49,50] . Classical centrality measurements rely on network topology.…”

Section: Influential Spreaders Identification Algorithms In Complex Nmentioning

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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Identifying influential spreaders by weighted LeaderRank

Cited by 224 publications

References 37 publications

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

A Community-Based Approach to Identifying Influential Spreaders

Identifying the influential spreaders in multilayer interactions of online social networks

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