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6th International Conference on Advances in Computing and Communications (ICACC)

作者： Gayathri, R. G. Nair, Jyothisha J. Kaimal, M. R. Amrita Sch Engn Dept Comp Sci & Engn Amritapuri 690525 India

Most of the real-world networks exhibit community structure, a property that reveals the existence of natural vertex clusters whose inter-edge density is lower than intra-edge density between various groups. Despite providing a better understanding of network structure and characteristics, community detection has many practical applications in diverse domains. Communities obtained from the telephone network provides many useful information that can be used for churn prediction, budget control in organizations etc. Detecting communities is a fundamental need in the area of networks, yet challenging. In this paper, we propose an extension to the Girvan-Newman algorithm for finding the betweenness using the transitive closure property and the greedy technique in Dijkstra's single source shortest path method. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： complex networks community detection gn algorithm edge betweenness transitive closure

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Extending Full Transitive Closure to Rank Removable Edges in gn algorithm

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Procedia Computer Science 2016年 93卷 995-1002页

作者： R.G. Gayathri Jyothisha J. Nair M.R. Kaimal Department of Computer Science and Engineering Amrita School of Engineering Amritapuri and 690525 India

关键词： complex networks community detection gn algorithm edge betweenness transitive closure

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Module partition of complex mechanical products based on weighted complex networks

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第4期30卷 1973-1998页

作者： Zhang, Na Yang, Yu Zheng, Yujie Su, Jiafu Chongqing Univ State Key Lab Mech Transmiss 174 Shazhengjie Chongqing 400044 Peoples R China

It is tough to build an effective mathematical model to describe the complicated relationships in complex mechanical products, which leads to the module partition of complex mechanical products and the guarantee of accurate results become more difficult. In addition, the module partition method cannot bring about a satisfactory module partition result if the scale of the products is huge and complicated. In this case, complex network theory is used to solve these problems in this paper. Firstly, a weighted complex network is established to systematically express the structure of complex mechanical products. In particular, customer demands are taken into account for module partition by introducing customer involvement. Secondly, the interval-valued intuitionistic fuzzy sets are used to calculate the relationships between parts for reducing the subjectivity of the calculation process. Afterwards, a modified gn algorithm (community detection algorithm) is proposed to achieve the module partition of complex mechanical products. Finally, the module partition of a wind turbine is carried out to verify the effectiveness of the proposed method in this paper. The result of the case study shows that the modified gn algorithm achieves better module partition performance than the classical gn algorithm and fuzzy clustering analysis method, which obtains a satisfactory result for applications.

关键词： Module partition Complex mechanical products Undirected weighted complex network gn algorithm

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Segmental Symbolic Execution Based on Clustering

Segmental Symbolic Execution Based on Clustering

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IEEE Conference on SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation

作者： Ma, Rui Gao, Haoran Dou, Bowen Wang, Xiajing Hu, Changzhen Beijing Inst Technol Sch Comp Sci & Technol Beijing Key Lab Software Secur Engn Technol Beijing Peoples R China

ISBN: (纸本)9781728140346

As the impact of security vulnerabilities on information systems becomes more and more serious, program analysis and vulnerability discovery techniques play an increasingly important role in the field of information security. Among many binary program analysis techniques, dynamic symbolic execution technology has been deeply researched and widely applied as an important automated test and vulnerability discovering technology in the information security field. Aimed at the existing problems in dynamic symbolic execution, this paper proposes a binary program segmental symbolic execution approach based on a clustering algorithm. Different from the previous approach of dividing the program segment according to the function process or method in the program, the proposed approach divides the program into larger segments by an improved gn algorithm, and then performs dynamic symbolic execution on each segment. Finally, the results are merged to complete the analysis of the entire program. In this paper, the approach is compared with the regular symbol execution using angr, and the experimental results show the effectiveness of the proposed approach in its time consumption, calculation and storage resource occupation.

关键词： program analysis segmental symbolic execution gn algorithm angr

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Personalized Community Discovery algorithm Based on gn 2017

Personalized Community Discovery Algorithm Based on GN

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6th International Conference on Network, Communication and Computing (ICNCC)

作者： Yu Xiao-ming Li Wen-jie Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China

ISBN: (纸本)9781450353663

Nowadays, complex network has become a hot topic with the development of Internet. How to further explore the structure of complex networks also becomes a current focus. Generally, network has a strong community structure, so we could find these community structures to help us understand characteristics of complex networks. As a representative of classic community discovery algorithms, gn algorithm could discover complex network communities. Traditional gn algorithm removes the edge with highest betweenness in networks, which gets partion results. This paper improves traditional gn algorithm by quantifying the relationship among users based on micro-blog. Our improved gn algorithm that removes edges with the lowest relationship applies to asymmetric networks. Verified by real social networks and simulated micro-blog networks, the improved gn algorithm could discover community structure more precisely and effectively. Compared with traditional gn algorithm, running time of our algorithm is less, which reduces time complexity.

关键词： complex network community discovery micro-blog community gn algorithm user relationship

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Mapreduce Model for Finding Closely Knit Communities in Large Scale Networks 6

Mapreduce Model for Finding Closely Knit Communities in Larg...

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IEEE International Conference on Communication and Signal Processing (ICCSP)

作者： Gayathri, R. G. Nair, Jyothisha J. Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Amritapuri 690525 India

ISBN: (纸本)9781509038008

In this paper, we propose a scalable method for finding the evolving communities in complex networks. The various network properties that are prominent in real-world networks are studied. The proposed algorithm computes the edge betweenness based on the transitive closure property combined with the greedy approach applied in Dijkstra's single source shortest path method. The major contribution is an improvement to gn algorithm in linear time for weighted undirected networks. The proposed algorithm is applied on Mapreduce to prove its scalability and enhance the performance in managing and analyzing the large scale networks. The experimentation of the distributed algorithm is tested on the private cluster and the results are as expected in the theoretical analysis.

关键词： Community detection complex networks edge betweenness gn algorithm mapreduce

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Network Structure Detection and Analysis of Shanghai Stock Market

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JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM 2015年第2期8卷 383-398页

作者： Wu, Sen Tuo, Mengjiao Xiong, Deying Univ Sci & Technol Beijing Beijing Peoples R China

Purpose: In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange) 180-index, a stock correlation network is built to find the intra-community and inter-community relationship. Design/methodology/approach: The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. gn algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds. Findings: The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of stock prices' fluctuation in the internal community is closer than in different ones. The result of community structure detection also reflects correlations among different industries. Originality/value: Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.

关键词： complex network stock market community structure gn algorithm

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Coordinated Optimization Against Parallel Flows in Multi-Infeed HVDC System 35

Coordinated Optimization Against Parallel Flows in Multi-Inf...

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IEEE Region 10 Conference

作者： Zhao, Kang Liu, Yutian Wang, Chunyi Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China State Grid Shandong Elect Power Co Jinan Peoples R China

ISBN: (纸本)9781479986415

With rapid development of UHV AC and DC transmission systems, the problem of parallel flows emerges in many multi-infeed HVDC receiving-end systems in China. In this paper, an optimization method which coordinates parallel flows and stability of multi-infeed HVDC system is present. An analytical equivalent of multi-infeed short circuit ratio (MISCR) is defined based on power flow model, which considers the impact of DC system control modes. An impact factor is defined to assess the degree of the influence of the operation of opening a chosen network branch on MISCR. Considering this impact factor, an improved gn algorithm is proposed to partition the lower-voltage power network, which produces a set of partition schemes. A comprehensive evaluation factor is defined to find the optimal partition scheme which has the best partition effect and the minimum harm on stability of multi-infeed HVDC system. A case study of an actual regional power system in China validates the superiority of the method present in this paper.

关键词： multi-infeed HVDC system multi-infeed short circuit ratio (MISCR) DC control modes parallel flows gn algorithm

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Research on Complex Network algorithm Optimization Community Detection Problem

Research on Complex Network Algorithm Optimization Community...

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2015 International Power,Electronics and Materials Engineering Conference(IPEMEC 2015)

作者： ZHENG Zhiqing Information Network Center Shandong Institute of Business and Technology

ISBN: (纸本)9781510806474

The community structure is one of the most universal and important attributes of complex network,and the community division research of complex network is to reasonably divide the community structure practically existed in the complex *** the thought and theoretical method of supernetwork into the community division research of complex *** at the deficiencies of present gn algorithm,combining theoretical method of standardization centricity from the perspective of supernetwork,a kind of new community division algorithm of complex network is constructed,and the new algorithm is verified and analyzed through the *** experimental result reveals that the new algorithm is improved and perfected to some extent in the division result compared with gn algorithm.

关键词： community structure supernetwork gn algorithm standardization centricity

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The Research of Community Partition based on Motif Modularity and Effectiveness Evaluation

The Research of Community Partition based on Motif Modularit...

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2012 IEEE 5th International Conference on Management Engineering & Technology of Statistics

作者： Hua Han School of Science Wuhan University of Technology Wuhan,PRChina Wanlu Liu School of Science Wuhan University of Technology Wuhan,PRChina

According to the existence of motif in complex network topology structure,motif modularity is obtained on the basis of motif structure characteristics and modularity *** improved gn algorithm is put forward then,the algorithm terminates when motif modularity achieves *** variation of information V is introduced to evaluate the algorithm’s *** smaller the V value,the higher the algorithm’s *** larger the V value,the lower the algorithm’s *** Dolphin network and Karate network are used for simulation *** results show that the community structure divided by improved gn algorithm is more conform to the fact,the superiority of improved gn algorithm is *** motif modularity can be extended to other algorithms,and it can also be applied to cope with directed and weighted networks,so that we can obtain best community partition in more types of networks.

关键词： motif community structure modularity variation of information gn algorithm

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