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District metered areas determination for water distribution networks using improved girvan-newman algorithm

AIN SHAMS ENGINEERING JOURNAL 2024年第5期15卷

作者： Jazayeri, Pedram Moeini, Ramtin Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

In general, partitioning networks into district metered areas (DMA) is known as a practical method to manage the pressure and reduce water leakage in Water Distribution Networks (WDNs). Different methods such as engineering judgment, optimization techniques, and graph theory are proposed to determine the DMA. The graph theory is a traditional method for partitioning networks including several algorithms. One of these algorithms is the girvan-newman (GN) algorithm which is based on the mathematical parameters without considering the characteristics of networks. In other words, the hydraulic quality and quantity condition of WDNs are not used for DMA determination. Therefore, in this research, a new method is proposed to improve the performance of the GN algorithm for DMA determination considering the quantities of water networks. For this purpose, the average values of nodal pressure and residual chlorine concentration are calculated and used simultaneously for the junction's weight determination. Then, the edge scores are calculated based on the junction's weights, and the edges with the maximum scores are removed until to reach the desired number of DMAs. For comparison purposes, here, the Demand-Driven Simulation Method (DDSM) and Head-Driven Simulation Method (HDSM) analyses are used for the analysis of the Poulakis WDN, selected as a case study. A comparison of the results shows that by using the proposed method, the average pressure values are decreased and the average residual chlorine concentration values are increased. In other words, the pressure values are decreased from 16.32% to 26.23% and the average residual chlorine concentration values are increased from 2.5% to 18.37% in comparison with the results of the standard form of the GN algorithm using both DDSM and HDSM analyses. Furthermore, by using the proposed method, in all scenarios, the hybrid reliability values increase in comparison with the GN algorithm. The results indicate the unique performanc

关键词： Water distribution network Pressure management District Metered Area Graph theory girvan-newman algorithm Residual chlorine concentration

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Revisit girvan-newman algorithm for Research Topic Analysis: An Application on Library and Information Science Studies

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图书资讯学刊 2023年第1期21卷 1-16页

作者： Szu-Chia Lo Chun-Chieh Wang

Research trend analysis gives the research community an essential chance to learn the past to support sustainable development. The topic of evolution analysis presents a chance to position the current research, linkages among research topics, and identify the research gap. In this study, the authors revisit a known mechanism, namely girvan-newman (GN) algorithm, and propose a new approach for research topic analysis. Based on the GN algorithm, author-keywords analysis approach, one-mode cluster, and duo GN algorithm analysis were suggested and applied to research topic analysis of Library and Information Science studies. The results show that the suggested approach could process major quantity materials and be able to avoid the possible distorted results gained by taking the small size of samples, or two-mode cluster, to ensure the validity of the results. The topics' hierarchy structure also suggests a different approach that could be used to deconstruct the linkages among research topics for future study.

关键词： Research Topic Evolution girvan-newman algorithm Library Information Science

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Module dividing for brain functional networks by employing betweenness efficiency

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第21-22期79卷 15253-15271页

作者： Jiao, Zhuqing Cai, Min Ming, Xuelian Cao, Yin Zou, Ling Wang, Shui-Hua Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Peoples R China Nanjing Med Univ Dept Neurol Changzhou Peoples Hosp 2 Changzhou 213003 Peoples R China Loughborough Univ Sch Architecture Bldg & Civil Engn Loughborough LE11 3TU Leics England Univ Leicester Dept Informat Univ Rd Leicester LE1 7RH Leics England

Traditional researches assume that brain functional networks are static during the entire scanning process of functional magnetic resonance image (fMRI) in the resting state. However, it is not difficult to ignore the dynamic interaction patterns of brain regions that essentially change across time. In this study, we take the internal weight information of the brain functional network as a calculation condition of module dividing for brain functional networks. The concept of betweenness efficiency is firstly proposed to improve girvan-newman (GN) algorithm for a better module dividing result, and the maximum modularity is used as a criterion to classify the brain functional network modules of normal subjects. The effect of the improved method is verified by controlling subjects, parameters, environment and other conditions. Then, the improved method was utilized to separate the modules in brain functional networks of normal subjects, and the template was used to divide the functional network of Alzheimer's disease (AD) patients and mild cognitive impairment (MCI) sufferers. The shortest path length of each module is calculated, and the experimental results are compared with the original GN and weighted GN algorithm improved by newman. The experimental results demonstrate that, the maximum modularity of the improved method is higher while the dividing effect is better under the same conditions. Meanwhile, the conclusion is consistent with the existing research results when the proposed method is applicable to the analysis of the shortest path length. These results illustrate the viewpoint that the proposed method of module dividing is feasible in the analysis of modular structure of brain functional network.

关键词： Brain functional network Betweenness efficiency girvan-newman algorithm Modularity Module dividing

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Intentional islanding method based on community detection for distribution networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第1期13卷 30-36页

作者： Zhang, Haiyan Peng, Minfang Palensky, Peter Hunan Univ Coll Elect & Informat Engn Changsha Hunan Peoples R China Delft Univ Technol Intelligent Elect Power Grids Dept Delft Netherlands

Complex network theory is introduced to solve the islanding problem in an emergency of distribution networks. In this study, the authors put forward an intentional islanding method based on community detection. In this method, a new index has been defined called electrical edge betweenness, on the strength of edge betweenness in complex networks, which fuses electrical characteristics with topological features of actual power lines. Based on the index, the girvan-newman algorithm is employed to detect the community structure of distribution networks. Through referring to the modularity value (function Q) and coherent generator groups, they can get a reasonable amount and regions of communities. Then the whole distribution network can be partitioned into several self-sustainable islands meeting the stable operation constraints. The effectiveness of the authors' proposed method is tested on a standard IEEE 118-bus system.

关键词： power distribution faults complex networks network theory (graphs) intentional islanding method community detection distribution networks complex network theory islanding problem electrical edge betweenness edge betweenness strength electrical characteristics topological features actual power lines girvan-newman algorithm community structure detection modularity value function Q coherent generator groups self-sustainable islands stable operation constraints standard IEEE 118-bus system

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Distributed process monitoring framework based on decomposed modified partial least squares

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2019年第12期97卷 3087-3100页

作者： Rong, Mengyu Shi, Hongbo Wang, Fan Tan, Shuai East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large-scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large-scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality-based distributed process monitoring scheme is proposed. Firstly, the girvan-newman (GN) algorithm in complex network divides process variables into multiple sub-blocks. Secondly, greedy algorithm-based high-dimensional mutual information (HDMI) is used to extract quality-related variables in each sub-block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality-related or not in each sub-block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub-blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme.

关键词： distributed monitoring girvan-newman algorithm mutual information decomposed modified PLS

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Community detection for cold start problem in personalization Community detection is large social network graphs based on users' structural similarities and their attribute similarities 16

Community detection for cold start problem in personalizatio...

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IEEE International Conference on Computer and Information Technology (CIT)

作者： Bhatia, Ankush Charotar Univ Sci & Technol U&PU Patel Dept Comp Engn Anand Gujarat India

ISBN: (纸本)9781509043149

A persona in a social network is defined as the person's activities and attributes in a social network as seen by others. And a community in a social network is defined as a group of users in that social network which share common interests and are most likely to interact with each other in the network. For community detection, a user's persona and its connections with the other users in a network, both play an important role. These communities help in personalization of a service/product which accommodates to a specific generalizing the service for all the users of that community.

关键词： clustering community cold start girvan-newman algorithm Louvain modularity personalization user persona

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RESEARCH ON THE algorithm OF COMMUNITY DISCOVERY BASED ON THE STANDARD DEVIATION OF EDGE - BETWEENNESS 13

RESEARCH ON THE ALGORITHM OF COMMUNITY DISCOVERY BASED ON TH...

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13th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Tang, Yanling Li, Jianping Zhao, Gaofeng Wang, Yangyang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781509061266

In this paper, we mainly do the research about the non-overlapping community discovery. We use the GN algorithm and LP algorithm to test the randomly generated network, and compare the two algorithms in two layers, which are the time complexity level, and the static network's synchronization capability level. The simulation results show that the two algorithms are applicable to different scenes.

关键词： Community discovery girvan-newman algorithm Label propagation algorithm Standard deviation of edge-betweenness

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Scalable Community Detection from Networks by Computing Edge Betweenness on MapReduce

Scalable Community Detection from Networks by Computing Edge...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Moon, Seunghyeon Lee, Jae-Gil Kang, Minseo Korea Adv Inst Sci & Technol Dept Knowledge Serv Engn Taejon 305701 South Korea

ISBN: (纸本)9781479939190

Community detection from social network data gains much attention from academia and industry since it has many real-world applications. The girvan-newman (GN) algorithm is a divisive hierarchical clustering algorithm for community detection, which is regarded as one of the most popular algorithms. It exploits the concept of edge betweenness to divide a network into multiple communities. Though it is being widely used, it has limitations in supporting large-scale networks since it needs to calculate the shortest path between every pair of nodes in a network. In this paper, we develop a parallel version of the GN algorithm to support large-scale networks. To this end, we propose a new algorithm, which we call Shortest Path Betweenness MapReduce algorithm (SPB-MRA), that utilizes the MapReduce model. This algorithm consists of four major stages, and all operations are executed in parallel. In addition, we suggest an approximation technique to further speed up community detection processes. We implemented SPB-MRA on Hadoop, which is the most popular open-source platform for MapReduce, and then conducted performance tests for SPB-MRA on Amazon EC2 instances. The results showed that elapsed time decreases almost linearly as the number of reducers increases and the approximation technique introduces negligible errors.

关键词： MapReduce Hadoop community detection edge betweenness girvan-newman algorithm SPB-MRA

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Block & Comovement Effect of Stock Market in Financial Complex Network

Block & Comovement Effect of Stock Market in Financial Compl...

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1st International Conference on Complex Sciences: Theory and Applications (Complex 2009)

作者： Du, Chongwei Wang, Xiong Qiu, Liyin Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Math Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Informat Secur Shanghai 200240 Peoples R China

ISBN: (纸本)9783642024689

In the work, we present a method to analyze block & comovement effect of stock market by finding out the community structure in the financial complex network. We choose the stocks from Shanghai and Shenzhen 300 Index as data source and convert them into the complex network in matrix format which is based on the measurements of correlation we proposed in this paper. The classical GN algorithm and the Net Draw tool are applied to obtain the modularity and draw all the community structures. The results of our work can offer not only the internal information about the capital flows in the stock market but also the prediction of variation and trend line of some stocks with delay-correlation.

关键词： block & comovement effect complex network community structure correlative coefficient girvan-newman algorithm faction delay-correlation prediction

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Enhancing Community Detection for Big Sensor Data Clustering via Hyperbolic Network Embedding

Enhancing Community Detection for Big Sensor Data Clustering...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Karyotis, Vasileios Tsitseklis, Konstantinos Sotiropoulos, Konstantinos Papavassiliou, Symeon Inst Commun & Comp Syst ICCS NTUA Sch Elect & Comp Engn Athens 15780 Greece

ISBN: (纸本)9781538632277

In this paper we present a novel big data clustering approach for measurements obtained from pervasive sensor networks. To address the potential very large scale of such datasets, we map the problem of data clustering to a community detection one. Datasets are cast in the form of graphs, representing the relations among individual observations and data clustering is mapped to node clustering (community detection) in the data graph. We propose a novel computational approach for enhancing the traditional girvan-newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, making it possible to compute more efficiently the hyperbolic edge-betweenness centrality (HEBC) needed in the modified GN algorithm. This allows for more efficient clustering of the nodes of the data graph without significantly sacrificing accuracy. We demonstrate the efficacy of our approach with artificial network and data topologies, and real benchmark datasets. The proposed methodology can be used for efficient clustering of datasets obtained from massive pervasive smart city/building sensor networks, such as the FIESTA-IoT platform, and exploited in various applications such as lower-cost sensing.

关键词： Data clustering Community detection girvan-newman algorithm Hyperbolic network embedding Rigel embedding Edge-betweenness centrality

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