Node Importance In Complex Networks Research Articles

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

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.

Analyzing the structure of earthquake network by [formula omitted]-core decomposition

Earthquake network is a novel methodology to analyze the relationships among the earthquake events. k-core decomposition is one of the common measures to identify the most important nodes in complex networks. In this paper, we first verify the hierarchical structure of the earthquake network, which is constructed by the method based on space–time influence domain, and use the k-core decomposition to visualize the topology in order to figure out the structure and the central part of earthquake networks. On observation of the evolution of the maximum coreness, it is found that it has some sudden changes with the occurrence of major shocks. The highest core, which has high clustering characteristic, is tend to be located in the area that can directly or indirectly cause the major shocks. By comparing the energy distribution in each shell, we find that the energy proportion of the highest core is high, especially before the major shocks.

Identification of important nodes in directed biological networks: a network motif approach.

Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

网络重要节点排序方法综述

The important nodes in complex networks are the extraordinary nodes which play more significant role than other nodes on the structure and function of the networks. In recent years, the reaserch on indentifying inflential nodes in complex networks has attracted much attention, because of its great theoretical significance as well as the wide range of applications. Aiming at different types of networks and motivated by different problems and applications, researchers have proposed groups of methods. This article systematically reviews more than 30 representative methods which are classified into four categories, and detailedly compares them from the aspects of computing ideas and application scenarios, and futher analyzes the strongness and weakness of each method. On this basis, this article summarizes the existing problems and outlines eight open issues as main challenges in the near future.

A Feature Selection Method from the View Of Complex Network

Feature selection as a common method of dimensionality reduction always is one of the hot topics in machine learning and data mining field. Classic algorithms don't consider features' global redundancies fully, which may cause classification accuracy on selected feature subset to be not high enough. For the weakness, we propose a feature selection method(FSCN) based on node importance estimation in complex networks and genetic algorithm, regarding each feature as a network node, creating edges according to mutual information, then the problem of feature selection is converted to estimate the node importance in complex networks, and choosing the best feature subset by genetic algorithm. As the experiment results show, our algorithm could find better feature selection subset which results in the lowdimensional data and the good classification accuracy.

A new method of identifying influential nodes in complex networks based on TOPSIS

In complex networks, identifying influential nodes is the very important part of reliability analysis, which has been a key issue in analyzing the structural organization of a network. In this paper, a new evaluation method of node importance in complex networks based on technique for order performance by similarity to ideal solution (TOPSIS) approach is proposed. TOPSIS as a multiple attribute decision making (MADM) technique has been an important branch of decision making since then. In addition, TOPSIS is first applied to identify influential nodes in a complex network in this open issue. In different types of networks in which the information goes by different ways, we consider several different centrality measures as the multi-attribute of complex network in TOPSIS application. TOPSIS is utilized to aggregate the multi-attribute to obtain the evaluation of node importance of each node. It is not limited to only one centrality measure, but considers different centrality measures, because every centrality measure has its own disadvantage and limitation. Then, we use the Susceptible–Infected (SI) model to evaluate the performance. Numerical examples are given to show the efficiency and practicability of the proposed method.

Node Importance Evaluation of Anti-Missile Networks

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.

A new clustering algorithm based on data field in complex networks

The evaluation of node importance in complex networks has been an increasing widespread concern in recent years. Seeking and protecting vital nodes is important to ensure the security and stability of the whole network. Existing clustering algorithms of complex networks all have certain drawbacks, which could not cover everything in calculation accuracy and time complexity, and need external supervision. To design a fast complex networks clustering method is a problem which requires to be solved immediately. This paper proposes a clustering algorithm of complex networks based on data field using physical data field theory, which excavates key nodes in complex networks by evaluating the importance of nodes based on a mutual information algorithm, and then uses it to classify the clusters. To verify the validity of the algorithm, a simulation experiment was conducted. The results indicated that the algorithm could analyze the cluster exactly and calculate with high-speed, it could also determine the granularity of a partition according to the actual demand.

Key nodes in complex networks identified by multi-attribute decision-making method

In complex networks, it is significant how to rank the nodes according to their importance. Most of the existing methods of ranking key nodes (e.g. degree-based, betweenness-based) only consider one factor but not the integration of whole complex network in evaluating the importance of nodes, so those methods each have a limited application range. In this paper, a multi-attribute decision-making method to identify the key nodes in complex networks is proposed. In our method, each node is regarded as a solution, and each importance evaluation criterion as one solution's attribute. After that, we calculate the closeness between each solution and the ideal solution in order to obtain the integration results of node importance in complex networks. The proposed method can be used in a variety of complex networks. It is also easy to evaluate the importance evaluation criteria. Finally, experimental results show that the proposed method is effective.

Identifying important nodes in weighted functional brain networks: A comparison of different centrality approaches

We compare different centrality metrics which aim at an identification of important nodes in complex networks. We investigate weighted functional brain networks derived from multichannel electroencephalograms recorded from 23 healthy subject under resting-state eyes-open or eyes-closed conditions. Although we observe the metrics strength, closeness, and betweenness centrality to be related to each other, they capture different spatial and temporal aspects of important nodes in these networks associated with behavioral changes. Identifying and characterizing of these nodes thus benefits from the application of several centrality metrics.

Multi-index evaluation algorithm based on principal component analysis for node importance in complex networks

The evaluation of vital nodes in complex networks has been an increasing widespread concern in recent years. Seeking and protecting vital nodes is important to ensure the security and stability of the whole network. Presently, most of the existing evaluation algorithms cannot completely reflect the circumstances of complex networks because these algorithms are proposed based on a certain characteristic of the network, and the single metric is incomplete and limited. With that said, the development of a new algorithm that evaluates node importance in complex networks is critical. First, a number of classic ranking measures for evaluating vital nodes have been demonstrated in the present study. Second, basic concepts of complex networks have been summarised further. Third, a new multi-index evaluation algorithm based on principal component analysis has been proposed. The algorithm can reveal the simple structure behind the complex data, and has no parameter restrictions to represent the features of the data. The algorithm presented in the current study synthesises the topological characteristics of the network, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, mutual-information and local clustering coefficient to reflect the relative node importance. Finally, to verify the validity of the algorithm, a simulation experiment was conducted. The results indicated that the algorithm is more rational, effective, complete and accurate than the algorithm of single metric evaluation.

Finding vital node by node importance evaluation matrix in complex networks

In order to evaluate the node importance in complex network, considering the disadvantages of node deletion method, node contraction method and betweenness method, through defining the node efficiency and the node importance evaluation matrix, a method to find the vital node in complex networks is proposed by using the node importance evaluation matrix. Considered in this method are the node efficiency, node degree and adjacent node importance contributions, and used adjacent node degree and efficiency value to characterize the contribution of their importance. Finally, an optimized algorithm whose time complexity was O(Rn2) is provided. Experiments show that this method is effective and feasible, and it is applicable to large scale complex networks.

Improved Method of Node Importance Evaluation Based on Node Contraction in Complex Networks

To solve the problem of node importance evaluation in complex networks, an improved method of node contraction based on agglomeration is presented. To start with, methods to evaluate the node importance in complex networks are analyzed. And secondly, evaluation of edge importance is introduced on the basis of method of node contraction. Node importance is defined as weighted sum of importance of node itself and edges that connected with node. Improved method to evaluate node importance considers the connection character of the node, and moreover, by regulating the coefficient, we can adjust the influence of edge on node importance. Finally, an illustrative example verifies the feasibility and validity of the improved method.