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The relative importance of structure and dynamics on node influence in reversible spreading processes

Frontiers of physics 2021年第5期16卷 211-220页

作者： Jun-Yi Qu Ming Tang Ying Liu Shu-Guang Guan School of Physics and Electronic Science East China Normal UniversityShanghai200241China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai200241China School of Computer Science Southwest Petroleum UniversityChengdu610500China

The reversible spreading processes with repeated infection widely exist in nature and human society,such as gonorrhea propagation and meme *** influential spreaders is an important issue in the reversible spreading dynamics on complex networks,which has been given much *** for structural centrality,the nodes’dynamical states play a significant role in their spreading influence in the reversible spreading *** integrating the number of outgoing edges and infection risks of node’s neighbors into structural centrality,a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node *** number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders,*** find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential ***,compared with the positive effect of the local structural characteristic,slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best *** understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.

关键词： reversible spreading process node influence local structure dynamical state

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Mathematical Modeling in Social Network Analysis： Using TOPSIS to Find node influences in a Social Network

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Journal of Mathematics and System Science 2013年第10期3卷 531-541页

作者： William P Fox Sean F. Everton Department of Defense Analysis Naval Postgraduate School Monterey CA 93943 USA

In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.

关键词： Social network analysis multi-attribute decision making Analytical hierarchy process (AHP) weighted criterion TOPSIS node influence

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A new metric for evaluating the importance of nodes in complex product structure networks

A new metric for evaluating the importance of nodes in compl...

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第40届中国控制会议

作者： Ruizhao Zheng Yong Zhang School of Information and Control Engineering China University of Mining and Technology

The importance of nodes is a significant metric for the structure of complex product networks. The existing metrics still have deficiencies such as relatively partial consideration of factors, subjective weight distribution among relevant factors, and lack of effective validation schemes. This paper proposed a node importance evaluation method based on Principal Component Analysis（PCA）. The local, global, and location attributes of the product network are taken into account. Meanwhile, a node importance verification method based on Spearman’s correlation coefficient was designed by considering the correlation between node importance and its propagation strength. Finally, the proposed algorithm’s effectiveness was verified on ten artificial networks and one real product structure using the SI propagation model.

关键词： Product design Product architecture networks Complex networks node influence

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