GFT centrality: A new node importance measure for complex networks

2017 ◽  
Vol 487 ◽  
pp. 185-195 ◽  
Author(s):  
Rahul Singh ◽  
Abhishek Chakraborty ◽  
B.S. Manoj
Keyword(s):  
Complex Networks ◽  
Importance Measure ◽  
Node Importance

scholarly journals Approximation of Interactive Betweenness Centrality in Large Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Sebastian Wandelt ◽  
Xing Shi ◽  
Xiaoqian Sun
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Betweenness Centrality ◽  
Shortest Paths ◽  
Importance Measure ◽  
Worst Case ◽  
Approximation Techniques ◽  
Computational Costs ◽  
Node Importance ◽  
Wide Range

The analysis of real-world systems through the lens of complex networks often requires a node importance function. While many such views on importance exist, a frequently used global node importance measure is betweenness centrality, quantifying the number of times a node occurs on all shortest paths in a network. This centrality of nodes often significantly depends on the presence of nodes in the network; once a node is missing, e.g., due to a failure, other nodes’ centrality values can change dramatically. This observation is, for instance, important when dismantling a network: instead of removing the nodes in decreasing order of their static betweenness, recomputing the betweenness after a removal creates tremendously stronger attacks, as has been shown in recent research. This process is referred to as interactive betweenness centrality. Nevertheless, very few studies compute the interactive betweenness centrality, given its high computational costs, a worst-case runtime complexity of O(N∗∗4) in the number of nodes in the network. In this study, we address the research questions, whether approximations of interactive betweenness centrality can be obtained with reduction of computational costs and how much quality/accuracy needs to be traded in order to obtain a significant reduction. At the heart of our interactive betweenness approximation framework, we use a set of established betweenness approximation techniques, which come with a wide range of parameter settings. Given that we are interested in the top-ranked node(s) for interactive dismantling, we tune these methods accordingly. Moreover, we explore the idea of batch removal, where groups of top-k ranked nodes are removed before recomputation of betweenness centrality values. Our experiments on real-world and random networks show that specific variants of the approximate interactive betweenness framework allow for a speedup of two orders of magnitude, compared to the exact computation, while obtaining near-optimal results. This work contributes to the analysis of complex network phenomena, with a particular focus on obtaining scalable techniques.

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.

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.

Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

2015 ◽  
Vol 29 (03) ◽  
pp. 1450268 ◽  
Author(s):  
Fang Hu ◽  
Yuhua Liu
Keyword(s):  
Discriminant Analysis ◽  
Complex Networks ◽  
Linear Discriminant Analysis ◽  
Eigenvector Centrality ◽  
Complex Situation ◽  
Linear Discriminant ◽  
Multi Index ◽  
Node Importance ◽  
Evaluation Algorithm ◽  
Important Nodes

The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate.

Node Importance in Controlled Complex Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 437-441 ◽  
Author(s):  
Jin Zhou ◽  
Xinghuo Yu ◽  
Jun-An Lu
Keyword(s):  
Complex Networks ◽  
Node Importance
Sign in / Sign up

Export Citation Format

Share Document