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community structure detection in social networks based on dictionary learning

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 structure detection in Complex Networks with Applications to Gas-Liquid Two-Phase Flow

Community Structure Detection in Complex Networks with Appli...

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

作者： Gao, Zhongke Jin, Ningde Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China

ISBN: (纸本)9783642024689

We propose an algorithm to detect community structure in complex networks based on data field theory. The efficiency and accuracy of the algorithm for computer-simulated and real networks make it feasible to be used for the accurate detection of community structure in complex networks. Using the conductance fluctuating signals measured from gas-liquid two-phase flow dynamic experiments, we construct the flow pattern complex network. With the applications of the community-detection algorithm to the flow pattern complex network, we achieve good identification of flow pattern in gas-liquid two-phase flow. In this paper, from a new perspective, we not only present a new community-detection algorithm based on data field theory, but also build a bridge between complex network and two-phase flow.

关键词： complex networks community structure detection data field theory two-phase flow pattern identification

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

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

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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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A new algorithm CNM-Centrality of detecting communities based on node centrality

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2016年 446卷 138-151页

作者： Hu, Fang Liu, Yuhua Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Hubei Univ Chinese Med Coll Informat Engn Wuhan 430065 Peoples R China

The discovery and analysis of community structure in complex networks is a hot issue in recent years. In this paper, based on the fast greedy clustering algorithm CNM with the thought of local search, the introduction of the idea of node centrality and the optimal division of the central nodes and their neighbor nodes into correct communities, a new algorithm CNM-Centrality of detecting communities in complex networks is proposed. In order to verify the accuracy and efficiency of this algorithm, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks by LFR-benchmark. The experimental results indicate that this algorithm can identify the communities accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity and NMI than the CNM, Infomap, Walktrap algorithms do. (C) 2015 Elsevier B.V. All rights reserved.

关键词： community structure detection Modularity Normalized mutual information CNM-Centrality algorithm Simulation test

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Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2013年第15期392卷 3260-3272页

作者： Ji, Junzhong Song, Xiangjing Liu, Chunnian Zhang, Xiuzhen Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing 100124 Peoples R China RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic Australia

community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Complex network community structure detection Ant colony clustering Fitness perception Pheromone diffusion model

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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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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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A Multiagent Evolutionary Method for Detecting Communities in Complex Networks

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COMPUTATIONAL INTELLIGENCE 2016年第4期32卷 587-614页

作者： Ji, Junzhong Jiao, Lang Yang, Cuicui Liu, Jiming Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing 100124 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

community structure detection in complex networks contributes greatly to the understanding of complex mechanisms in many fields. In this article, we propose a multiagent evolutionary method for discovering communities in a complex network. The focus of the method lies in the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the community detection problem. First, the method uses a connection-based encoding scheme to model an agent and a random-walk behavior to construct a solution. Next, it applies three evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents and solution evolution. We tested the performance of our method using synthetic and real-world networks. The results show its capability in effectively detecting community structures.

关键词： complex network community structure detection multiagent evolutionary method random walk

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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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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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