Ranking spreaders by decomposing complex networks

Zeng, An; Zhang, Chengjun

doi:10.1016/j.physleta.2013.02.039

Cited by 383 publications

(186 citation statements)

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“…Also, Chen et al [27] introduced a so-called ClusterRank method, which takes the influence of neighbor nodes and clustering coefficient into consideration. Zeng and Zhang [28] improved the established k-shell method by rethinking the significant connections between nodes and removed nodes and proposed a mixed degree decomposition method. Pei et al [29] confirmed that the most influential actors are situated in the k-core across disparate social platforms.…”

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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“…In recent years, many scholars have extended and improved K-core. For example, Zeng et al [21] considered the information of the Ks degree of removed nodes after Ks decomposition, and they proposed the mixed degree decomposition method (MMD). Garas et al [22] developed Ks decomposition for weighted networks.…”

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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“…It was in 2013 that Zeng and Zhang proposed a modification of k-shell method called the mixed degree decomposition (MDD) [8]. The method is described as follows.…”

Section: Mixed Degree Decomposition Methodsmentioning

confidence: 99%

“…Importance of nodes in the transmission of information is a matter of great concern in literature of information diffusion in networks [8][9][10]. As mentioned in the previous paragraph, some nodes that have large number of connections are highly important in the sense that any piece of information in their hands may spread faster than that in the hands of others.…”

Section: Introductionmentioning

confidence: 99%

Spreading Information in Complex Networks: An Overview and Some Modified Methods

Karunakaran¹,

Manuel²,

NarayananSatheesh³

2018

Graph Theory - Advanced Algorithms and Applications

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The knowledge of node's ability and importance in spreading information in a complex network is important for developing efficient methods either to decelerate spreading in the case of diseases or to accelerate spreading in the case of information flow, which would benefit the whole population. Some systems are highly affected by a small fraction of influential nodes. Number of fast and efficient spreaders in a network is much less compared to the number of ordinary members. Information about the influential spreaders is significant in the planning for the control of propagation of critical pieces of information in a social or information network. Identifying important members who act as the fastest and efficient spreaders is the focal theme of a large number of research papers. Researchers have identified approximately 10 different methods for this purpose. Degree centrality, closeness centrality, betweenness centrality, k-core decomposition, mixed degree decomposition, improved k-shell decomposition, etc., are some of these methods. In this expository article, we review all previous works done in the field of identifying potential spreaders in a network.

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Ranking spreaders by decomposing complex networks

Cited by 383 publications

References 50 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

Spreading Information in Complex Networks: An Overview and Some Modified Methods

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