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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zhang, Yihan Wu, Jiahui Wang, Jia School of Electrical Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9798350382426

With the growing prevalence of distributed photovoltaic power plants, various issues arising from photovoltaic adoption are becoming more apparent. This study focuses on stabilizing grid-connected voltage in photovoltaic areas through an examination of a voltage partitioning control method based on the modularity function, which takes into account the compensation device's reactive power compensation capability and the entire network's active network losses. The aim is to quickly regulate voltage to allowable limits while reducing active network losses caused by voltage regulation. When applying the voltage control strategy to the improved IEEE33 node model, the simulation findings demonstrate that this method facilitates optimal usage of each reactive power compensation piece of equipment in the photovoltaic (PV) system while decreasing unneeded equipment losses by minimizing frequent actions among devices. The results approve the viability of the control method proposed. © 2023 IEEE.

关键词： aggregate domain modularity function photovoltaic grid-connection reactive power support capability voltage-coordinated control

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Algorithm for predicting weighted protein complexes by using modularity function

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年 1-12页

作者： Zhu, Yun Zhao, Xincan Chen, Yeh-Cheng Hu, Yu-Xi Liu, Yinping Mao, Yimin Jiangxi Univ Sci & Technol Sch Sci Fac Ganzhou 341000 Jiangxi Peoples R China Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Northwestern Polytech Univ Sch Software Xian Shaanxi Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China

Due to the difficulties in obtaining the identification results of protein complexes with higher accuracy, as well as the running efficiency of the protein complexes identification algorithm based on weighted modularity function clustering is low, a novel protein complex recognition algorithm named IWPC-MF (Algorithm for identifying weighted protein complexes based on modularity function) was proposed. Firstly, we build the TCEWS (topological clustering for weighted edges) algorithm, which is founded on improved edge clustering coefficient and the combination of Pearson correlation coefficient with edge point clustering coefficient. Secondly, seed nodes were selected according to the weight of nodes, then the IMS (initial module selection strategy) program has been proposed. The similarity measurement and the protein attachment degree between nodes were selected to obtain the initial clustering module by traversing neighbors of seeds. Finally, based on the tightness, we take the IMC (initial module combination strategy) module with modularity function was added to initial module and finally complete the protein complex detection, which can overcome the defect that the traditional modularity function cannot identify the overlapped and small complex. IWPC-MF algorithm was used to identify protein complexes on DIP (the Database of Interacting Proteins) data, in order to reduce effectively the computational complexity and randomness, speed up the clustering speed. The experimental results show that IWPC-MF algorithm has better performance on accuracy and recall, which is more reasonable to identify protein complexes.

关键词： Protein-protein interaction network modularity function Initial module Protein complex

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A cloud-edge collaborative optimization control strategy for voltage in distribution networks with PV stations

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 167卷

作者： Li, Guoqing Wang, Wei Pang, Dan Wang, Zhipeng Tan, Weixian Wang, Zhenhao Ge, Jinming Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Jilin 132012 Peoples R China State Grid Changchun Power Supply Co Changchun 130021 Peoples R China

With the continuous expansion of the power system scale and the continuous development of the power network, the traditional power system management and optimization methods face many challenges. In order to meet the requirements of voltage optimization and adjustment, the optimization problem is divided into cloud front precomputation and edge computing device cooperative optimization computation with the framework of cloudedge cooperation. The cloud front-end precomputation uses an improved reactive-voltage sensitivity based on an improved modularity function to partition the power system on a 15 min basis and stores the results in the cloud data memory. The voltage threshold device detects the node voltage overrun and triggers the collaborative optimization computation of the edge computing devices, which sends a command to the cloud to call the partitioning result of this time period, and the cloud sends the result to each edge computing device, which determines the area it is responsible for, and adjusts the voltage overrun partitioning by using the mixed-integer second-order conic planning, and ultimately realizes the optimization strategy within the minute-level zone. Since the voltage adjustment is a fine-grained optimization of the local area, it is highly flexible and targeted. Moreover, using the cloud-edge collaboration technology, the intelligent management and optimization of the power system is finally realized. Case analysis and comparative verification show that the method proposed in this paper is accurate and highly efficient.

关键词： Cloud edge synergy Reactive-voltage sensitivity modularity function Mixed-integer second-order cone planning

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Detecting Community Structure in Complex Networks with Backbone Guided Search Algorithm 19th

Detecting Community Structure in Complex Networks with Backb...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Zeng, Rong-Qiang Xue, Li-Yuan Basseur, Matthieu Chengdu Univ Informat Technol Dept Comp Sci Chengdu 610225 Sichuan Peoples R China Chinese Acad Sci Chengdu Documentat & Informat Ctr Chengdu 610041 Sichuan Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Univ Angers LERIA Blvd Lavoisier F-49045 Angers 01 France

ISBN: (纸本)9789819947546;9789819947553

Detecting communities is of great importance in complex networks, which is very hard and not yet satisfactorily solved. This paper investigates a backbone guided search algorithm in order to optimize the modularity of the community structure in complex networks. Our proposed algorithm consists of two main procedures, which are local search and backbone guided search. When the local search procedure can not improve the modularity function any more, the backbone guided search procedure is applied for further improvements. The computational results indicate the effectiveness of the proposed algorithm compared with the classical methods.

关键词： complex networks community detection modularity function local search backbone guided search

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modularity Based Community Detection in Hypergraphs 1

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18th International Workshop on Algorithms and Models for the Web Graph (WAW)

作者： Kaminski, Bogumil Misiorek, Pawel Pralat, Pawel Theberge, Francois SGH Warsaw Sch Econ Decis Anal & Support Unit Warsaw Poland Poznan Univ Tech Inst Comp Sci Poznan Poland Toronto Metropolitan Univ Dept Math Toronto ON Canada Tutte Inst Math & Comp Ottawa ON Canada

ISBN: (数字)9783031322969

ISBN: (纸本)9783031322952;9783031322969

In this paper, we make a significant step toward designing a scalable community detection algorithm using hypergraph modularity function. The main obstacle with adjusting the initial stage of the classical Louvain algorithm is dealt via carefully adjusted linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. It remains to properly tune the algorithm and design a mechanism to adjust the weights in the modularity function (in an unsupervised way), depending on how often nodes in one community share hyperedges with nodes from other communities. It will be done in the journal version of this paper.

关键词： Community Detection Algorithm Hypergraphs modularity function

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Catchment classification using community structure concept: application to two large regions

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2021年第3期35卷 561-578页

作者： Tumiran, Siti Aisyah Sivakumar, Bellie Univ Malaysia Sabah Fac Sci & Nat Resources Jalan UMS Kota Kinabalu 88400 Sabah Malaysia Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia Indian Inst Technol Dept Civil Engn Mumbai 400076 Maharashtra India

The present study applies the concept of community structure to classify catchments in two large regions: Australia and the United States. Specifically, the edge betweenness method is applied to monthly streamflow data from a network of 218 stations across Australia and from a network of 639 stations across the United States. The influence of streamflow correlation threshold (i.e. spatial correlation in streamflow between streamflow stations) on catchment classification is examined, through use of different thresholds, suitable for each region, as appropriate. The results reveal that, for both regions, a very small number of communities have a large number of catchments within them (for instance, considering both regions as small as 16-18% of the largest communities combine to represent as much as 70-75% of the catchments), and a significantly large number of communities have only a very few catchments within them (for instance, almost 70% of the communities have only one or two stations within them, and thus represent only about 20% and 10% of the catchments in Australia and the US, respectively). An interpretation of the identified catchment communities in terms of catchment characteristics (station drainage area, station stream length, and station elevation) and flow properties (mean and coefficient of variation) is also made. The catchment classification is also explained using the correlation-distance relationship between the stations.

关键词： Catchment classification Streamflow Complex networks Community structure Edge betweenness method modularity function

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Fuzzy Clustering in Assortative and Disassortative Networks

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021年第6期25卷 989-999页

作者： Kojima, Ryoichi Legaspi, Roberto Murofushi, Toshiaki KDDI Res Inc 2-1-15 Ohara Fujimino Saitama 3568502 Japan Tokyo Inst Technol Sch Comp Dept Math & Comp Sci Meguro Ku 2-12-1-W8-49 Ookayama Tokyo 1528550 Japan

Despite the significance of assortativity as a property of networks that paves for the emergence of new structural types, surprisingly, there has been little research done on assortativity. Assortative networks are perhaps among the most prominent examples of complex networks believed to be governed by common phenomena, thereby producing structures far from random. Further, certain vertices possess high centrality and can be regarded as significant and influential vertices that can become cluster centers that connect with high membership to many of the surrounding vertices. We propose a fuzzy clustering method to meaningfully characterize assortative, as well as disassortative, networks by adapting the Bonacichi's power centrality to seek the high degree centrality vertices to become cluster centers. Moreover, we leverage our novel modularity function to determine the optimal number of clusters, as well as the optimal membership among clusters. However, due to the difficulty of finding real-world assortative network datasets that come with ground truths, we evaluated our method using synthetic data but possibly bearing resemblance to real-world network datasets as they were generated by the Lancichinetti-Fortunato-Radicchi benchmark. Our results show our non-hierarchical method outperforms a known hierarchical fuzzy clustering method, and also performs better than a well-known membership-based modularity function. Our method proved to perform beyond satisfactory for both assortative and disassortative networks.

关键词： fuzzy clustering modularity function assortative network disassortative network

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modularity of the ABCD random graph model with community structure

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JOURNAL OF COMPLEX NETWORKS 2022年第6期10卷 cnac050-cnac050页

作者： Kaminski, Bogumil Pankratz, Bartosz Pralat, Pawel Theberge, Francois SGH Warsaw Sch Econ Decis Anal & Support Unit Al Niepodlegsci 162 PL-02554 Warsaw Poland Toronto Metropolitan Univ Dept Math 50 Victoria St Toronto ON M5B 2K3 Canada Tutte Inst Math & Comp Ottawa ON K1J 0B9 Canada

The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR (Lancichinetti, Fortunato, Radicchi) one, and its main parameter $\xi$ can be tuned to mimic its counterpart in the LFR model, the mixing parameter $\mu$. In this article, we investigate various theoretical asymptotic properties of the ABCD model. In particular, we analyse the modularity function, arguably, the most important graph property of networks in the context of community detection. Indeed, the modularity function is often used to measure the presence of community structure in networks. It is also used as a quality function in many community detection algorithms, including the widely used Louvain algorithm.

关键词： random graphs models of complex networks communities modularity function

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An efficient graph clustering algorithm in signed graph based on modularity maximization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2019年第11期30卷

作者： Zhuo, Kefan Yang, Zhuoxuan Yan, Guan Yu, Kai Guo, Wenqiang Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Xinjiang Univ Finance & Econ Sch Comp Sci & Engn Urumqi 830012 Xinjiang Peoples R China

The unsigned graphs containing positive links only, have been analyzed fruitfully. However, the physical relations behind complex networks are dissimilar. We often encounter the signed networks that have both positive and negative links as well. It is very important to study the characteristics of complex networks and predict individual attitudes by analyzing the attitudes of individuals and their neighbors, which can divide individuals into different clusters or communities. To detect the clusters in signed networks, first, a modularity function for signed networks is proposed on the basis of the combination of positive and negative part. Then, a new graph clustering algorithm for signed graphs has also been proposed based on CNM algorithm, which has high efficiency. Finally, the algorithm has been applied on both artificial and the real networks. The results show that the proposed method has been able to achieve near-perfect solution, which is suitable for multiple types real networks.

关键词： Signed networks cluster configuration modularity function optimization.

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Find modules in signed networks based on modularity optimization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2019年第32期33卷

作者： Yu, Kai Yu, Yongping Wu, Lei Liu, Di Guo, Wenqiang Xinjiang Univ Finance & Econ Sch Informat Management Urumqi 830012 Xinjiang Peoples R China Xinjiang Univ Finance & Econ Urumqi 830012 Xinjiang Peoples R China

The signed network depicts individual cooperative or hostile attitude in a system. It is very important to study the characteristics of complex networks and predict individual attitudes by analyzing the attitudes of individuals and their neighbors, which can divide individuals into different modules or communities. To detect the modules in signed networks, first, a modularity function for signed networks is utilized on the basis of the existing modularity function. Then, a new module detection algorithm for signed networks has also been put forward, which has high efficiency. Finally, the algorithm has been applied on both artificial and real networks. The results show that the number of modules given by our proposed algorithm is consistent with that of the number of actual modules.

关键词： Signed networks module structure modularity function optimization

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