scholarly journals A Generalization of the Importance of Vertices for an Undirected Weighted Graph

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 902
Author(s):  
Ronald Manríquez ◽  
Camilo Guerrero-Nancuante ◽  
Felipe Martínez ◽  
Carla Taramasco
Keyword(s):  
Weighted Graph ◽  
Ranking Method ◽  
Global Information ◽  
Undirected Edge ◽  
Weighted Networks ◽  
Node Importance ◽  
Importance Ranking

Establishing a node importance ranking is a problem that has attracted the attention of many researchers in recent decades. For unweighted networks where the edges do not have any attached weight, many proposals have been presented, considering local or global information of the networks. On the contrary, it occurs in undirected edge-weighted networks, where the proposals to address this problem have been more scarce. In this paper, a ranking method of node importance for undirected and edge-weighted is provided, generalizing the measure of line importance (DIL) based on the centrality degree proposed by Opsahl. The experimentation was done on five real networks and the results illustrate the benefits of our proposal.

scholarly journals Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuanzhi Yang ◽  
Lei Yu ◽  
Zhongliang Zhou ◽  
You Chen ◽  
Tian Kou
Keyword(s):  
Decision Making ◽  
Complex Networks ◽  
Multicriteria Decision Making ◽  
Practical Significance ◽  
Network Stability ◽  
Weighting Method ◽  
Multicriteria Decision ◽  
Node Importance ◽  
Subjective Factor ◽  
Importance Ranking

Measuring node importance in complex networks has great theoretical and practical significance for network stability and robustness. A variety of network centrality criteria have been presented to address this problem, but each of them focuses only on certain aspects and results in loss of information. Therefore, this paper proposes a relatively comprehensive and effective method to evaluate node importance in complex networks using a multicriteria decision-making method. This method not only takes into account degree centrality, closeness centrality, and betweenness centrality, but also uses an entropy weighting method to calculate the weight of each criterion, which can overcome the influence of the subjective factor. To illustrate the effectiveness and feasibility of the proposed method, four experiments were conducted to rank node importance on four real networks. The experimental results showed that the proposed method can rank node importance more comprehensively and accurately than a single centrality criterion.

scholarly journals Node importance ranking of complex networks

2013 ◽  
Vol 62 (17) ◽  
pp. 178901
Author(s):  
Liu Jian-Guo ◽  
Ren Zhuo-Ming ◽  
Guo Qiang ◽  
Wang Bing-Hong
Keyword(s):  
Complex Networks ◽  
Node Importance ◽  
Importance Ranking

Application and Research of Weighted Network Node Load Evaluation Method Based on Network Contribution Degree

2014 ◽  
Vol 926-930 ◽  
pp. 1874-1877
Author(s):  
Yun Peng Zhang
Keyword(s):  
Evaluation Method ◽  
Load Flow ◽  
Network Node ◽  
Weighted Network ◽  
Weighted Networks ◽  
Node Importance ◽  
Critical Nodes ◽  
The One ◽  
Load Evaluation

At present, most of research on node importance evaluation is concentrated on static un-weighted network. For weighted networks, this paper presented a new evalution method of node importance based on load flow in the node-weighted network, and it was based on the contribution of the nodes for the whole network in the view of microscopic and macroscopic. The most important node was the one which was determined by the outputing load, inputing load and crossing load between the different nodes. The improved evaluation method could help exactly to find some critical nodes which ware sensitive to the efficiency of networks. Final, example verifies its efficiency and feasibility.

scholarly journals Target Importance Ranking Method Based on Non-Return Mapping Analysis

2018 ◽  
Vol 1069 ◽  
pp. 012043
Author(s):  
Hanghang You ◽  
Minjian Yu ◽  
Qisong Han ◽  
Yan Lv ◽  
Hao Meng
Keyword(s):  
Ranking Method ◽  
Return Mapping ◽  
Mapping Analysis ◽  
Importance Ranking

The Solution to Node Importance in Complex Networks Based on PageRank Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1777-1780
Author(s):  
Guang Li Li ◽  
Han Li ◽  
Ye Ran Wang ◽  
Tong Bo Zhang
Keyword(s):  
Complex Networks ◽  
Network Science ◽  
Evaluation Model ◽  
Pagerank Algorithm ◽  
Weighted Networks ◽  
Node Importance ◽  
Optimized Model ◽  
Modified Algorithm

Network science is an emerging interdiscipline that can solve practical issues by researching and analyzing properties of complex networks. In this paper, a modified algorithm based on PageRank is developed to construct the evaluation model in directed-weighted networks. This model can effectively determine the node importance in networks. Discussions about how to construct networks with the use of relationship between data and how to determine the weight of edges form the major concern of the present studies. Schemes for solving the above issues can differ in different networks. We successfully apply this model to citation, co-author and weibo follower networks. Through the results, we find that this optimized model can efficiently evaluate the importance of nodes in different complex networks and has strong applicability.

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