作者机构：
Department of Computer Science,Central China Normal University,Wuhan,430079,China;Information Engine
会议名称：
The 13th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2014)(第十三届分布式计算及其应用国际学术研讨会)
会议时间：
2014-11-24
会议地点：
湖北咸宁
会议论文集名称：
The 13th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2014)(第十三届分布式计算及其应用国际学术研讨会) 论文集
关键词：
Complex Network;Node importance;Multi-index evaluation;Locally Linear Embedding
摘要：
Evaluation of node importance in complex network is significant,so it is important to seek and protect important node,which is ensure the security and stability of the entire network.At present,most algorithms of important node evaluation are according to the single-index,which can’t reflect the whole condition of complex network.In this paper,synthesizing multi-index factors of node importance,including degree centrality,betweenness centrality,closeness centrality,eigenvector centrality,mutual-information,etc.,a new multi-index evaluation algorithm based on Locally Linear Embedding (LLE) for the node importance in complex network is proposed.In order to verify the validity of this algorithm,a series of simulation experiments have been done.Through comprehensive analysis,the simulation results represent that the new algorithm is rational,effective,integral and accurate.
摘要：
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.
期刊：
Journal of Communications,2015年10(7):503-511 ISSN：1796-2021
通讯作者：
Liu, Yuhua
作者机构：
[Fang Hu; Yuhua Liu] Schoolof Computer Science, Central China Normal University, Wuhan, China;[Fang Hu] College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China
通讯机构：
Schoolof Computer Science, Central China Normal University, Wuhan, China
期刊：
Journal of Algorithms & Computational Technology,2015年9(4):427-448 ISSN：1748-3018
通讯作者：
Liu, Yuhua(yhliu@mail.ccnu.edu.cn)
作者机构：
[Fang Hu; Yuhua Liu; Jianzhi Jin] School of Computer Science, Central China Normal University, Wuhan, China;[Fang Hu] College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China
通讯机构：
[Fang Hu; Yuhua Liu*] S;School of Computer Science, Central China Normal University, Wuhan, 430079, China<&wdkj&>College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, China<&wdkj&>School of Computer Science, Central China Normal University, Wuhan, 430079, China
摘要：
Identification of important nodes is an emerging hot topic in complex networks over the last few years. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness, closeness, etc. At present, most algorithms of important node evaluation are based on the single-indicator, which can't reflect the whole condition of the complex network. Therefore, in this paper, after choosing multiple indicators from degree centrality, closeness centrality, eigenvector centrality, information centrality, density/clustering coefficient, mutual-information centrality, etc., and a new multi-indicator evaluation algorithm based on Locally Linear Embedding (LLE) for identifying important nodes in complex network is proposed. This proposed algorithm is compared with some single-indicator algorithms and other mainstream multi-indicator algorithms based on real-world networks. Through comprehensive analysis, the experimental results show that the proposed method performs quite well in evaluating the importance of nodes, and it is rational, effective, integral and accurate.
作者机构：
[Hu, Fang; Liu, Yuhua; Jin, Jianzhi] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Hu, Fang] Hubei Univ Chinese Med, Inst Informat Engn, Wuhan 430065, Peoples R China.
会议名称：
13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)
会议时间：
NOV 24-27, 2014
会议地点：
Xian Ning, PEOPLES R CHINA
会议主办单位：
[Hu, Fang;Liu, Yuhua;Jin, Jianzhi] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.^[Hu, Fang] Hubei Univ Chinese Med, Inst Informat Engn, Wuhan 430065, Peoples R China.
会议论文集名称：
International Symposium on Distributed Computing and Applications to Business Engineering & Science
关键词：
Complex Network;Node importance;Multi-index evaluation;Locally Linear Embedding
摘要：
Evaluation of node importance in complex network is significant, so it is important to seek and protect important node, which is ensure the security and stability of the entire network. At present, most algorithms of important node evaluation are according to the single-index, which can't reflect the whole condition of complex network. In this paper, synthesizing multi-index factors of node importance, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, mutual-information, etc., a new multi-index evaluation algorithm based on Locally Linear Embedding (LLE) for the node importance in complex network is proposed. In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results represent that the new algorithm is rational, effective, integral and accurate.
期刊：
Lecture Notes in Electrical Engineering,2014年277 LNEE(1):1253-1262 ISSN：1876-1100
通讯作者：
Liu, Y.(yhliu@mail.ccnu.edu.cn)
作者机构：
[Xu, Ke; Liu, Yuhua; Xu, Cui] School of Computer, Central China Normal University, Wuhan 430079, China;[Xu, Kaihua] College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China
会议名称：
3rd International Conference on Computer Engineering and Network, CENet 2013
摘要：
By analyzing the problem of k-means, we find the traditional k-means algorithm suffers from some shortcomings, such as requiring the user to give out the number of clusters k in advance, being sensitive to the initial cluster centers, being sensitive to the noise and isolated data, only being applied to the type found in globular clusters, and being easily trapped into a local solution et cetera. This improved algorithm uses the potential of data to find the center data and eliminate the noise data. It decomposes big or extended cluster into several small clusters, then merges adjacent small clusters into a big cluster using the information provided by the Safety Area. Experimental results demonstrate that the improved k-means algorithm can determine the number of clusters, distinguish irregular cluster to a certain extent, decrease the dependence on the initial cluster centers, eliminate the effects of the noise data and get a better clustering accuracy.
摘要：
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.
作者机构：
[Xu, Cui; Xu, Ke; Liu, Yuhua] Cent China Normal Univ, Acad Comp Sci, Wuhan 430079, Hubei, Peoples R China.;[Xu, Kaihua] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Hubei, Peoples R China.
会议名称：
International Conference on Information Science and Technology (ICIST)
会议时间：
MAR 23-25, 2013
会议地点：
Yangzhou, PEOPLES R CHINA
会议主办单位：
[Xu, Cui;Liu, Yuhua;Xu, Ke] Cent China Normal Univ, Acad Comp Sci, Wuhan 430079, Hubei, Peoples R China.^[Xu, Kaihua] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Hubei, Peoples R China.
会议论文集名称：
International Conference on Information Science and Technology
摘要：
Clustering analysis is a hot research in the field of complex network, in order to overcome high time complexity, difficulty for the user to select initial conditions and other defects of the existing clustering algorithms, this paper analyses the above problems and proposes an adaptive clustering algorithm based on data field in complex networks. First, the importance factor is proposed to dig out the important vertices in networks as the center of the cluster which is based on the defects and merits of evaluation indexes of the vertex's degree, mutual information and closeness respectively. Due to the vertices in networks connected and react upon one another, the theory of data field in physics was introduced into complex networks, by calculating field-strength and potential function of vertices to realize clustering of vertices-cluster topology structure division. Simulation experiments show that the adaptive algorithm can get approximate optical cluster topology structures with a low time complexity, and has a higher accuracy and validity compared to other algorithms.
摘要：
In order to improve the energy efficiency and avoid the energy hole problem in wireless sensor networks, this paper proposes a routing algorithm named RCBDF (Ring and Clustering Based on Data Filed). The algorithm uses the concept of physical field and the ring network. The routing algorithm will achieve a better balance between energy consumption and network life by accounting the rate of the node remaining energy and the size of the cluster. Meantime, double cluster head rotation was introduced to cluster in this algorithm. Simulation analysis shows that: the routing policy will balance the energy consumption of the network at a large extent, greatly extend the network lifetime and avoid the energy hole phenomenon.
摘要：
Clustering is useful for discovering groups and identifying interesting distributions in the underlying data. At present, k-means algorithm as a method of clustering based on the partition has more applications. By analyzing the problem of k-means, we find the traditional k-means algorithm suffers from some shortcomings, such as requiring the user to give out the number of clusters k in advance, being sensitive to the initial cluster centers, being sensitive to the noise and isolated data, only being applied to the type found in globular clusters, and being easily trapped into a local solution et cetera. This improved algorithm uses the potential of data field to find the center data and eliminate the noise data. It decomposes big or extended cluster into several small clusters, then merges adjacent small clusters into a big cluster using the information provided by the Safety Area. Experimental results demonstrate that the improved k-means algorithm can determine the number of clusters, distinguish irregular cluster to a certain extent, decrease the dependence on the initial cluster centers, eliminate the effects of the noise data and get a better clustering accuracy.
作者：
Kai Hua Xu;Di Zhang;Yu Hua Liu;Ke Xu;Yuan Hao Xi
期刊：
Applied Mechanics and Materials,2013年347-350:628-633 ISSN：1662-7482
通讯作者：
Liu, Y. H.(yhliu@mail.ccnu.edu.cn)
作者机构：
[Kai Hua Xu] College of Physical Science and Technology, Central China Normal University, Wuhan, China;[Yuan Hao Xi] Institute of Information, Liaoning University, Shenyang, China;[Di Zhang; Ke Xu; Yu Hua Liu] School of Computer, Central China Normal University, Wuhan, China
期刊：
Journal of Communications,2013年8(12):862-869 ISSN：1796-2021
通讯作者：
Liu, Y.(yhliu@mail.ccnu.edu.cn)
作者机构：
[Fang Hu; Jianzhi Jin; Yuhua Liu] Department of Computer Science, Central China Normal University, Wuhan, 430079, China;[Kaihua Xu] College of Physical Science and Technology, Central China Normal University, Wuhan, 430079, China
通讯机构：
Department of Computer Science, Central China Normal University, China
关键词：
Community structures;Complex network models;Current research status;Evolution modeling;Hierarchical community structures;Network structures;Power-law behavior;Scale free networks;Cellular automata;Complex networks;Electric network topology;Social sciences;Computer simulation
