In a social network, spreading information through the powerful leader of the community is highly valuable. Applying effective methods to identify influential nodes will leverage cellular networks in device-to-device local communication. In this study, entropy of betweenness centrality (EBC) combining the degree of the node and the degree of the neighbour node is proposed as a metric to select nodes from large and complex networks to disseminate information easily. EBC describes how the node is essential to connect two regions of the network. In the authors' work, nodes are identified by EBC, which is evaluated by susceptible-infected (SI) model. Hence, if the source of infection in the SI model is one node and the percentage of infected nodes is the largest, which means that the source node is the most powerful node in the network. Simulation results show that the proposed method performs better than the existing methods to spread information in the mobile social network.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2016.1433

Cluster head selection method for content-centric mobile social network in 5G

Many real-world networks, including biological networks, internet, information and social networks can be modeled by a complex network consisting of a large number of elements connected to each other. One of the important issues in complex networks is the evaluation of node importance because of its wide usage and great theoretical significance, such as in information diffusion, control of disease spreading, viral marketing and rumor dynamics. A fundamental issue is to identify a set of most influential individuals who would maximize the influence spread of the network. In this paper, we propose a novel algorithm for identifying influential nodes in complex networks with community structure without having to determine the number of seed nodes based on genetic algorithm. The proposed algorithm can identify influential nodes with three methods at each stage (degree centrality, random and structural hole) in each community and measure the spread of influence again at each stage. This process continues until the end of the genetic algorithm, and at the last stage, the most influential nodes are identified with maximum diffusion in each community. Our community-based influencers detection approach enables us to find more influential nodes than those suggested by page-rank and other centrality measures. Furthermore, the proposed algorithm does not require determining the number of k initial active nodes.

http://www.jise.ir/article_111693_882106c3440c82cff80ab5c2c2499e6f.pdf

Influence maximization in complex social networks based on community structure

Most recent surveys and reviews on Influential Node Ranking Methods (INRMs) hightlight discussions on the methods' technical details, but there still lacks in-depth research on the fundamental issue of how to verify the considerable influence of these nodes in a network. Compared to conventional verification models such as cascade failure and linear threshold, the epidemic model is more widely used. Accordingly, we conducted a survey of INRM based on epidemic model on 81 primary studies and analyzed their Capability and Correctness which we defined in our work. Our study categorized 4 types of networks used by INRM, classified 7 categories of INRMs for analyzing the networks and defined 2 evaluation metrics set of Capability and Correctness for evaluating INRM from Ranking Rationality Perspective. We also discussed particular real-world networks that were used to evaluate INRM and the Capability and Correctness of different INRMs on ranking nodes in specific networks. This is, as far as we know, the first survey aimed at systematically summarizing the Capability and Correctness of INRM. Our findings can assist practitioners and researchers in choosing and comparing INRMs and identifying research gaps.

pdf/epidemic-model-based-network-influential-node-ranking-3uw33t8s.pdf

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Abstract The fusion entropy of Shannon entropy and permutation distribution entropy (PDE), which denotes dynamic Shannon entropy (DySEn), has been used to identify local abnormal interval signals of time series. Deng entropy, which is an extended entropy of Shannon entropy, has better performance in describing uncertainty. In this paper, dynamic Deng entropy (DyE d ) is proposed by fusing Deng entropy and PDE to identify local anomalies of time series. In order to illustrate the effectiveness and superiority of the proposed method, Deng entropy, DySEn and (DyEd) are used to detect abnormal signals under six different periodic conditions, and the recognition rate η is set to compare the detection effect of three methods. The results show that the recognition rate η of our method is much higher than the other two methods in the experiments that original signal is generated by sin +0.2*Gaussian white noise (GWN), logistic map(a=4)+0.2*GWN, 0.2*GWN, logistic map(a=4)+0.2*GWN respectively and sin(randn), sin(2t) produce abnormal signal. And in the other two anomaly recognition experiments that original signal is sin, sin + +logistic map(a=4)+0.2*GWN and local shuffle, sin(2t) engender abnormal interference, the recognition rate of the new entropy is equal to that of the DySEn method, but the recognition rate η of the new method is always the largest. This indicates that dynamic Deng entropy is an effective method for signal anomaly detection with periodic background. 

A novel method for local anomaly detection of time series based on multi entropy fusion

Physica A: Statistical Mechanics and its Applications: Editorial note

In complex networks, how to identify influential nodes in complex networks is a hot topic. Recently, weights of nodes and degree of nodes are combined for identifying influential nodes in the weighted networks. Degree centrality, closeness centrality and betweenness centrality are the most basic measures for describing the influence of nodes. In this paper, degree centrality, closeness centrality and betweenness centrality are considered. The three measures are built three basic probability assignment (BPAs) based on evidence theory, respectively. Then, a final measure, which is used to identify influence of nodes, is obtained by combining the three BPAs. Numerical examples are used to illustrate the effectiveness of the proposed method.

A new method for identifying influential nodes based on D-S evidence theory

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A new method for identifying influential nodes based on D-S evidence theory