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Multi-objective Population-Based Incremental Learning for community structure detection

Multi-objective Population-Based Incremental Learning for Co...

IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ma, Wenping Wu, Yue Yun, Jie Xidian Univ Int Res Ctr Intelligent Percept & ComputatMinist Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China

ISBN: (纸本)9781509006229

The community structure detection of complex networks has become a hot topic in the past several years. In this paper, a new discrete framework of population-based incremental learning for complex networks problem is proposed. Based on the proposed discrete framework, a novel multi-objective population-based incremental learning algorithm is proposed to solve community structure detection problem. The proposed algorithm combines population-based incremental learning with the multi-objective evolutionary algorithm based on decomposition, this makes the evolution get directionality and converge fast. In order to discourage premature convergence, a random perturbation operator is adopted. The proposed algorithm has two contradictory objective functions termed as negative ratio association and ratio cut, respectively. The community structure detection results are a set of tradeoff solutions by simultaneous optimizing these two contradictory objective functions. Each of these solutions corresponds to a network community structure at one hierarchical level. Experiments on both real-world and synthetic networks prove the effectiveness of the proposed algorithm.

关键词： community structure detection multi-objective evolutionary algorithm population based incremental learning random perturbation operator

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community structure detection in social networks based on dictionary learning

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Science China(Information Sciences) 2013年第7期56卷 182-193页

作者： ZHANG ZhongYuan School of Statistics Central University of Finance and Economics

Discovering community structures is a fundamental problem concerning how to understand the topology and the functions of complex network. In this paper, we propose how to apply dictionary learning algorithm to community structure detection. We present a new dictionary learning algorithm and systematically compare it with other state-of-the-art models/algorithms. The results show that the proposed algorithm is highly effectively at finding the community structures in both synthetic datasets, including three types of data structures, and real world networks coming from different areas.

关键词： community structure detection dictionary learning least squares error regression nonnegative matrix factorization

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community detection in Social Networks Using a Local Approach Based on Node Ranking

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IEEE ACCESS 2024年 12卷 92892-92905页

作者： Sheykhzadeh, Jafar Zarei, Bagher Soleimanian Gharehchopogh, Farhad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh *** Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Shabestar Branch Shabestar *** Iran

community detection is crucial for analyzing the structure of social networks and extracting hidden information from them. The goal is to find groups of nodes (communities) with high intra-group and low inter-group communications. This problem is NP-hard, and most existing algorithms are global with high computational complexity, especially for large networks. Recently, local methods with acceptable computational complexity have been developed, but many have low accuracy and are non-deterministic. This paper introduces a new local algorithm, LCD-SN, which identifies communities based on first- and second-degree neighbor nodes. Unlike other local algorithms, LCD-SN is highly accurate, definitive, and not dependent on initial seed nodes. Additionally, a new index is proposed to determine the importance of network nodes using their local characteristics (first- and second-degree neighbors). Using this index, LCD-SN first identifies important nodes, forms initial communities with these nodes and their first-degree neighbors, and then obtains final communities through post-processing. Experiments show that LCD-SN is effective in identifying communities in social networks.

关键词： Indexes Social networking (online) Clustering algorithms Partitioning algorithms Accuracy Chaotic communication Time complexity community structure detection modularity nodes ranking social network analysis

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Topology-distance-based clustering method for water distribution network partitioning

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AQUA-WATER INFRAstructure ECOSYSTEMS AND SOCIETY 2023年第11期72卷 2186-2198页

作者： Du, Kun Li, Jiangyun Xu, Wei Liu, Zilian Zheng, Feifei Kunming Univ Sci & Technol Fac Civil Engn & Mech Kunming 650500 Peoples R China Kunming Univ Sci & Technol Intelligent Infrastructure Operat & Maintenance Te Kunming 650500 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

Partitioning water distribution networks (WDNs) into district metered areas offers benefits including reduced nonrevenue water and simplified pressure management. However, current research in this field tends to narrowly focus on the topological relationships among nodes, often overlooking the influence of pressure reducing valves (PRVs) and pump stations on clustering results. To address this limitation, this study introduces a topology-distance-based clustering (TDBC) method that enhances the accuracy of partitioning by explicitly considering the impact of PRVs and pump stations. In the TDBC method, the Floyd algorithm is initially employed to construct a topological distance matrix that quantifies the degree of node connectivity. By amplifying topological distances for links including PRVs and pump stations, their effect on clustering results is incorporated. Subsequently, nodes are clustered using the K-means algorithm based on the resulting topology-distance matrix. The proposed TDBC approach is applied to four network cases, and its outcomes are compared with those of two traditional methods. The comparative analysis indicates that the TDBC algorithm achieves precise partitioning results for networks incorporating PRVs or pump stations, while ensuring a harmonious balance between modularity and the uniformity of the partitioning results, even in networks with complex structures and highly interconnected loops.

关键词： community structure detection K-means clustering algorithm partitioning water distribution network

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Distributed simultaneous state and parameter estimation of nonlinear systems

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2022年 181卷 74-86页

作者： Liu, Siyu Yin, Xunyuan Liu, Jianbang Liu, Jinfeng Ding, Feng Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada Nanyang Technol Univ Sch Chem & Biomed Engn 62 Nanyang Dr Singapore 637459 Singapore Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

In this paper, we consider distributed simultaneous state and parameter estimation for a class of nonlinear systems, for which the augmented model comprising both the states and the parameters is only partially observable. Specifically, we first illustrate how the sensitivity analysis (SA) can select variables for simultaneous state and parameter estimation. Then, a community structure detection (CSD) based process decomposition method is proposed for dividing the entire system into interconnected subsystems as the basis of distributed estimation. Next, we develop local moving horizon estimators based on the configured subsystem models, and the local estimators communicate with each other to exchange their estimates. Finally, an SA and CSD based distributed moving horizon estimation (DMHE) mechanism is proposed. The effectiveness of the proposed approach is illustrated using a chemical process consisting of four connected reactors. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Subsystem decomposition Sensitivity analysis community structure detection Distributed estimation Nonlinear process

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A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory

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NEW GENERATION COMPUTING 2022年第1期40卷 285-310页

作者： Zarei, Bagher Meybodi, Mohammad Reza Masoumi, Behrooz Islamic Azad Univ Fac Comp & Informat Technol Engn Shabestar Branch Shabestar Iran Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran

In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net.

关键词： Evolutionary algorithm Cellular evolutionary algorithm Cellular automata Learning automata Cellular learning automata Chaos theory community structure detection

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community detection based process decomposition and distributed monitoring for large-scale processes

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AICHE JOURNAL 2022年第11期68卷 e17826-e17826页

作者： Yin, Xunyuan Qin, Yan Chen, Hongtian Du, Wenli Liu, Jinfeng Huang, Biao Nanyang Technol Univ Sch Chem & Biomed Engn Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada Minist Educ East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai Peoples R China

Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large-scale processes. A data-driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two-layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner-subsystem interactions and inter-subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process.

关键词： community structure detection data-driven process decomposition distributed process monitoring fault detection large-scale processes

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Detecting community structure in complex networks using genetic algorithm based on object migrating automata

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COMPUTATIONAL INTELLIGENCE 2020年第2期36卷 824-860页

作者： Zarei, Bagher Meybodi, Mohammad Reza Islamic Azad Univ Qazvin Branch Fac Comp & Informat Technol Engn Qazvin Iran Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran

community structure is an important topological feature of complex networks. Detecting community structure is a highly challenging problem in analyzing complex networks and has great importance in understanding the function and organization of networks. Up until now, numerous algorithms have been proposed for detecting community structure in complex networks. A wide range of these algorithms use the maximization of a quality function called modularity. In this article, three different algorithms, namely, MEM-net, OMA-net, and GAOMA-net, have been proposed for detecting community structure in complex networks. In GAOMA-net algorithm, which is the main proposed algorithm of this article, the combination of genetic algorithm (GA) and object migrating automata (OMA) has been used. In GAOMA-net algorithm, the MEM-net algorithm has been used as a heuristic to generate a portion of the initial population. The experiments on both real-world and synthetic benchmark networks indicate that GAOMA-net algorithm is efficient for detecting community structure in complex networks.

关键词： community structure community structure detection complex networks modularity optimization

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Subsystem decomposition of process networks for simultaneous distributed state estimation and control

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AICHE JOURNAL 2019年第3期65卷 904-914页

作者： Yin, Xunyuan Liu, Jinfeng Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach. (c) 2018 American Institute of Chemical Engineers

关键词： subsystem decomposition distributed state estimation distributed control nonlinear process community structure detection

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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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