Efficient Identification of Node Importance Based on Agglomeration in Cycle-Related Networks

2020 ◽  
Vol 31 (07) ◽  
pp. 969-978
Author(s):  
Aysun Asena Kunt ◽  
Zeynep Nihan Berberler
Keyword(s):  
Complex Networks ◽  
Network Robustness ◽  
Practical Significance ◽  
Contraction Method ◽  
Node Importance ◽  
Different Types ◽  
Novel Method ◽  
Important Nodes

The identification of node importance in complex networks is of theoretical and practical significance for improving network robustness and invulnerability. In this paper, the importance of each node is evaluated and important nodes are identified in cycles and related networks by node contraction method based on network agglomeration. This novel method considers both the degree and the position of the node for the identifying the importance of the node. The effectiveness and the feasibility of this method was also validated through experiments on different types of complex networks.

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.

scholarly journals An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qibo Sun ◽  
Guoyu Yang ◽  
Ao Zhou
Keyword(s):  
Complex Networks ◽  
Disease Transmission ◽  
Evaluation Method ◽  
Weighted Average ◽  
Network Attack ◽  
Complex Network Theory ◽  
Node Importance ◽  
Different Characteristics ◽  
Important Nodes ◽  
Self Adaptive

Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

scholarly journals Seeking the Important Nodes of Complex Networks in Product R&D Team Based on Fuzzy AHP and TOPSIS

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhang ◽  
Qingpu Zhang ◽  
Hamidreza Karimi
Keyword(s):  
Complex Networks ◽  
Fuzzy Ahp ◽  
Evaluation Criteria ◽  
Multiple Attribute Decision Making ◽  
Eigenvector Centrality ◽  
Analytic Hierarchy ◽  
Node Importance ◽  
Multiple Attribute ◽  
Order Preference ◽  
Important Nodes

How to seek the important nodes of complex networks in product research and development (R&D) team is particularly important for companies engaged in creativity and innovation. The previous literature mainly uses several single indicators to assess the node importance; this paper proposes a multiple attribute decision making model to tentatively solve these problems. Firstly, choose eight indicators as the evaluation criteria, four from centralization of complex networks: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality and four from structural holes of complex networks: effective size, efficiency, constraint, and hierarchy. Then, use fuzzy analytic hierarchy process (AHP) to obtain the weights of these indicators and use technique for order preference by similarity to an ideal solution (TOPSIS) to assess the importance degree of each node of complex networks. Finally, taking a product R&D team of a game software company as a research example, test the effectiveness, operability, and efficiency of the method we established.

A novel method to evaluate node importance in complex networks

2019 ◽  
Vol 526 ◽  
pp. 121118 ◽  
Author(s):  
Yuanzhi Yang ◽  
Lei Yu ◽  
Xing Wang ◽  
Zhongliang Zhou ◽  
You Chen ◽  
...  
Keyword(s):  
Complex Networks ◽  
Node Importance ◽  
Novel Method

A HIGH ROBUSTNESS AND LOW COST CASCADING FAILURE MODEL BASED ON NODE IMPORTANCE IN COMPLEX NETWORKS

2014 ◽  
Vol 28 (02) ◽  
pp. 1450011 ◽  
Author(s):  
WEN-LI FAN ◽  
ZHI-GANG LIU ◽  
PING HU
Keyword(s):  
Complex Networks ◽  
Low Cost ◽  
Capacity Allocation ◽  
Connected Components ◽  
Cascading Failures ◽  
Allocation Model ◽  
Model Based ◽  
Node Importance ◽  
Optimal Capacity ◽  
Important Nodes

In this paper, we investigate the trade-off problem between the high robustness against cascading failures and the low costs of network constructions in complex networks. Since the important nodes with highly connected components usually play a key role in the network structure and network dynamics, we propose an optimal capacity allocation model based on node importance. The novel model will increase the capacities of those important nodes but reduce the network construction cost with the appropriate capacity allocation parameter. Moreover, we also discover that our matching model can enhance the robustness against cascading failures on the IEEE 300 network.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals Identification and Quantification of Node Criticality through EWM–TOPSIS: A Study of Hong Kong’s MTR System

2021 ◽  
Author(s):  
Yifan Zhang ◽  
S. Thomas Ng
Keyword(s):  
Hong Kong ◽  
Public Transportation ◽  
Early Stage ◽  
Network Robustness ◽  
Global Network ◽  
Practical Significance ◽  
Topsis Method ◽  
Urban Resilience ◽  
Network Efficiency ◽  
Entropy Weight

AbstractPublic transport networks (PTNs) are critical in populated and rapidly densifying cities such as Hong Kong, Beijing, Shanghai, Mumbai, and Tokyo. Public transportation plays an indispensable role in urban resilience with an integrated, complex, and dynamically changeable network structure. Consequently, identifying and quantifying node criticality in complex PTNs is of great practical significance to improve network robustness from damage. Despite the proposition of various node criticality criteria to address this problem, few succeeded in more comprehensive aspects. Therefore, this paper presents an efficient and thorough ranking method, that is, entropy weight method (EWM)–technology for order preference by similarity to an ideal solution (TOPSIS), named EWM–TOPSIS, to evaluate node criticality by taking into account various node features in complex networks. Then we demonstrate it on the Mass Transit Railway (MTR) in Hong Kong by removing and recovering the top k critical nodes in descending order to compare the effectiveness of degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), and the proposed EWM–TOPSIS method. Four evaluation indicators, that is, the frequency of nodes with the same ranking (F), the global network efficiency (E), the size of the largest connected component (LCC), and the average path length (APL), are computed to compare the performance of the four methods and measure network robustness under different designed attack and recovery strategies. The results demonstrate that the EWM–TOPSIS method has more obvious advantages than the others, especially in the early stage.

Motion estimation in vehicular environments based on Bayesian dynamic networks

2021 ◽  
pp. 1-12
Author(s):  
Lauro Reyes-Cocoletzi ◽  
Ivan Olmos-Pineda ◽  
J. Arturo Olvera-Lopez
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Ground Truth ◽  
Change Of Direction ◽  
Prediction Rate ◽  
Different Types ◽  
Novel Method ◽  
Comparison Of The Results ◽  
Multiple Obstacles ◽  
Real Traffic

The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work.

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