An Improved Weighted LeaderRank Algorithm for Identifying Influential Spreaders in Complex Networks

Zhang, Zhenghao; Jiang, Guoping; Song, Yurong; Xia, Lingling; Chen, Qi

doi:10.1109/cse-euc.2017.146

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Zhang et al [188] introduced an improved weighted LeaderRank method that considers indegree, clustering coefficient, and neighbors' influences. The method employs a function that depicts the links' weights based on the influences of nodes.…”

Section: ) Leaderrank-based Analysis Techniquementioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-k influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type analysis scope analysis approach analysis category analysis sub-category analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-k and influential spreader nodes in social networks.

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Section: ) Leaderrank-based Analysis Techniquementioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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“…When observing the reliability of the SOS, we find that the reliability value of each service component has a different influence on the fluctuation of the system reliability value, then, we add the service reliability influence ability (SD i ) in the process of iteration. In addition, many studies have shown that service components have different propagation [21,22], when calculating the LeaderRank value, we introduce the Betweenness centrality (BC i ) to control the service propagation capability. Symbol explanations are shown in Table 1.…”

Section: Iw-leaderrankmentioning

confidence: 99%

“…In fact, the weighted LeaderRank algorithm can't solve all the problems. Therefore, many solutions have been proposed in recent years, such as combining node propagation ability [21,22], considering the strength of association between the nodes [23].…”

Section: Introduction To Leaderrank Algorithmmentioning

confidence: 99%

Critical Components Identification for Service-Oriented Systems

Zhang¹,

Jiang²,

Qiao³

et al. 2019

Symmetry

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Identifying critical components are of great significance to the overall reliability of service-oriented systems (SOSs). As the size of the SOS increases, identifying critical components in the process of predicting the SOS reliability can reduce the number of components that need to be predicted and shorten the prediction time. Moreover, predicting the reliability of critical components can also ensure the stability of the SOS. Therefore, we transform the relationships among service components of the SOS into a service dependency graph. Then, an improved weighted LeaderRank algorithm (IW-LeaderRank) is proposed to measure the importance of components and obtain the sequence of critical components. Through experimental analysis, the method can accurately and efficiently identify critical components in SOSs.

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“…For improving the speed of identifying influencers, a weighting mechanism is introduced to the LeaderRank, allowing nodes with more fans to get more scores from the ground node [14]. Zhang et al [15] have proposed an improved weighted LeaderRank by taking clustering coefficient into account to depict the weight to identify influencers. Existing researches [16][17][18] show that structure entropy can be used to classify data based on ranking.…”

Section: Introductionmentioning

confidence: 99%

Identifying Key Classes Algorithm in Directed Weighted Class Interaction Network Based on the Structure Entropy Weighted LeaderRank

Jiang

Dai

2020

Mathematical Problems in Engineering

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Identifying key classes can help software maintainers quickly understand software systems. The existing key class recognition algorithms consider the weight of class interaction, but the weight mechanism is single or arbitrary. In this paper, the multitype weighting mechanism is considered, and the key classes are accurately identified by using four kinds of interaction. By abstracting the software system into the directed weighted class interaction network, a novel Structure Entropy Weighted LeaderRank of identifying key classes algorithm is proposed. First, considering multiple types and directions of interactions between every pair of classes, the directed weighted class interaction software network (DWCIS-Network) is built. Second, Class Entropy of each class is initialized by the software structural entropy in DWCIS-Network; the Structure Entropy Weighted LeaderRank applies the biased random walk process to iterate Class Entropy. Finally, the iteration is completed to obtain the Final Class Entropy ( FCE ) of each class as the importance score of each class, top- k classes are obtained, and key classes are identified. For two sets of experiments on Ant and JHotDraw, our approach effectively identifies key classes in class-level software networks for different top- k of classes, and the recall rates of our approach are the highest, 80% and 100%, respectively. From top-15% to top-5%, the precision of our approach is improved by 13.39%, which is the highest in comparison with the precisions of the other two classical approaches. Compared with the best performance of the two classical approaches, the RankingScore of our approach is improved by 16.51% in JHotDraw.

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An Improved Weighted LeaderRank Algorithm for Identifying Influential Spreaders in Complex Networks

Cited by 11 publications

References 26 publications

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Critical Components Identification for Service-Oriented Systems

Identifying Key Classes Algorithm in Directed Weighted Class Interaction Network Based on the Structure Entropy Weighted LeaderRank

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