A New Method for Evaluating Node Importance in Complex Networks Based on Data Field Theory

2010 ◽  
Author(s):  
Lv Le ◽  
Yu Hewei
Keyword(s):  
Field Theory ◽  
Complex Networks ◽  
New Method ◽  
Node Importance ◽  
Data Field

An Evaluation Method of Node Importance in Complex Network

2014 ◽  
Vol 602-605 ◽  
pp. 3597-3600
Author(s):  
Rui Sun ◽  
Wan Bo Luo
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Experimental Analysis ◽  
Evaluation Method ◽  
Theory And Practice ◽  
Theoretical Physics ◽  
Node Importance ◽  
Data Field ◽  
Meaningful Research

The evaluation of node importance is a very meaningful research in complex networks. This paper analyze the characteristics of complex network and consider the effects of nodes for the evaluation of node importance, introduces the idea of data field in theoretical physics and establishes the evaluation method of node importance based on topological potential in complex network. Through the theoretical and experimental analysis, it is proved that this method can evaluate the importance of node in complex network in a fast and accurate way, which is significant both to theory and practice.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

Identifying node importance based on information entropy in complex networks

2013 ◽  
Vol 88 (6) ◽  
pp. 065201 ◽  
Author(s):  
Fan Wenli ◽  
Liu Zhigang ◽  
Hu Ping
Keyword(s):  
Complex Networks ◽  
Information Entropy ◽  
Node Importance

Node Importance Evaluation of Anti-Missile Networks

2013 ◽  
Vol 765-767 ◽  
pp. 1098-1102
Author(s):  
Yu Xia ◽  
Fei Peng
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Evaluation Method ◽  
Simulation Experiments ◽  
Node Importance ◽  
Correlation Degree ◽  
Evaluation Algorithm

in order to improve the efficiency and validity of node importance evaluation, a new evaluation method for node importance in complex networks was proposed based on node approach degree and node correlation degree. The basic idea of the method is that the larger the approach degree of a node is, the closer to center of a complex network the node is and the more important it is; the bigger the correlation degree of a node is, the more important the node is. An evaluation algorithm corresponding to the method was designed for the warship fleet cooperation anti-missile network. Finally, the validity of the proposed method was verified by simulation experiments.

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

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