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Complex system decomposition for distributed state estimation based on weighted graph

CHEMICAL ENGINEERING RESEARCH & DESIGN 2019年 151卷 10-22页

作者： Zhang, Langwen Yin, Xunyuan Liu, Jinfeng South China Univ Technol Coll Automat Sci & Technol Guangzhou 510641 Guangdong Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada

In this work, a systematic subsystem decomposition procedure for complex process systems is proposed for distributed state estimation. In the proposed procedure, both the connectivity within a system and the strength of connections are taken into account. A nonlinear process is first presented by a directed graph in which a node denotes either a state or a measured output of the nonlinear process. In the directed graph, nodes are connected via weighted edges and the weights reflect the strength of connection between two nodes. Based on the weighted directed graph, community structure detection is used to divide all the variables into smaller groups, such that the intra-connection within each group is made much stronger than the interaction among different groups. Subsystem models that are appropriate for distributed state estimation are configured based on the variables assigned to the groups. A comparative study between the proposed procedure with a procedure based on unweighted directed graph is conducted through extensive simulations. In the simulations, a numerical example and a chemical process example are considered. Three commonly used distributed state estimation algorithms designed based on the two decomposition procedures are applied to the two examples. A few guidelines on when to use weighted graphs for decomposition for distributed state estimation are concluded. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Subsystem decomposition Distributed state estimation Weighted directed graph community structure detection

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Unsupervised cluster analyses of character networks in fiction: community structure and centrality

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KNOWLEDGE-BASED SYSTEMS 2019年 163卷 800-810页

作者： Chen, R. H. -G. Chen, C. -C. Chen, C. -M. Natl Taiwan Normal Univ Dept Phys Taipei Taiwan Univ British Columbia Dept Comp Sci Vancouver BC Canada Minnan Normal Univ Minnan Cultural Res Inst Zhangzhou Peoples R China Longyan Univ Res Inst Cent Soviet Dist Longyan Peoples R China

We present an integrated approach to cluster and visualize character networks in fiction with the aid of computational and statistical methods. An unsupervised clustering algorithm, minimum span clustering (MSC), was applied to cluster fictional characters at various characteristic resolutions based on their activities in the novel. As a demonstration, we study the character network in Dream of the Red Chamber, the greatest novel in Chinese literature. The character network of the novel is found to exhibit properties of scale-free and small-world networks. Based on unsupervised cluster analyses, we construct and visualize the community structure of the network, and find a three-tiered structure of core, secondary, and peripheral characters. By treating the network as a weighted graph, we further analyze the centralities of characters to determine their importance in the network, and find that betweenness centrality, as a measure of characters' control over the flow of the narrative, is differentiated from other centrality measures for Dream of the Red Chamber. We believe that these analytic methods provide beneficial tools for applications such as autonomous novel writing. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Unsupervised clustering Computer-aided visualization Social network community structure detection Centrality measures

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How do countries specialize in agricultural production? A complex network analysis of the global agricultural product space

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ENVIRONMENTAL RESEARCH LETTERS 2020年第12期15卷

作者： Campi, Mercedes Duenas, Marco Fagiolo, Giorgio Univ Buenos Aires CONICET Inst Interdisciplinario Econ Polit Buenos Aires I Buenos Aires DF Argentina Univ Bogota Jorge Tadeo Lozano Dept Econ Int Trade & Social Policy Bogota Colombia Scuola Super Sant Anna Ist Econ Pisa Italy

Using a complex-network perspective, this paper empirically explores the determinants of the process through which countries, given their capabilities, specialize in agricultural production. Using production data from the Food and Agriculture Organization (FAO) for the period 1993-2013, we characterize the agricultural production space as a time-sequence of bipartite networks, connecting countries to the agricultural products they produce. We then project this representation in the agricultural production spaces, linking countries or products according to their similarity in production profiles, and we identify properties and determinants underlying their evolution. We find that, despite the unprecedented pressure that food systems have been undergoing in recent years, the agricultural production space is a very dense network displaying well-defined and stable communities of countries and products. We also show that the observed country community structures are not only shaped by environmental conditions, but also by economic, socio-political, and technological factors. We conclude by discussing the implications of such findings on our understanding of the complex relationships involving production capabilities and specialization patterns.

关键词： Food systems agricultural production specialization bipartite networks community structure detection hypergeometric filtering

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Unsupervised methods for Software Defect Prediction 19

Unsupervised methods for Software Defect Prediction

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10th International Symposium on Information and Communication Technology (SoICT)

作者： Ha, Duy-An Chen, Ting-Hsuan Yuan, Shyan-Ming Natl Chiao Tung Univ EECS Int Grad Program Hsinchu Taiwan Natl Chiao Tung Univ Inst Comp Sci & Engn Hsinchu Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781450372459

Software Defect Prediction (SDP) aims to assess software quality by using machine learning techniques. Recently, by proposing the connectivity-based unsupervised learning method, Zhang et al. have been proven that unsupervised classification has great potential to apply to this problem. Inspiring by this idea, in our work we try to replicate the results of Zhang et al.'s experiment and attempt to improve the performance by examining different techniques at each step of the approach using unsupervised learning methods to solve the SDP problem. Specifically, we try to follow the steps of the experiment described in their work strictly and examine three other clustering methods with four other ways for feature selection besides using all. To the best of our knowledge, these methods are first applied in SDP to evaluate their predictive power. For replicating the results, generally results in our experiments are not as good as the previous work. It may be due to we do not know which features are used in their experiment exactly. Fluid clustering and spectral clustering give better results than Newman clustering and CNM clustering in our experiments. Additionally, the experiments also show that using Kernel Principal Component Analysis (KPCA) or Non-Negative Matrix Factorization (NMF) for feature selection step gives better performance than using all features in the case of unlabeled data. Lastly, to make replicating our work easy, a lightweight framework is created and released on Github.

关键词： Software Defect Prediction Unsupervised Learning community structure detection Software Engineering Machine Learning

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Discovery of Society structure in A Social Network Using Distributed Cache Memory 5

Discovery of Society Structure in A Social Network Using Dis...

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5th International Conference on Web Research (ICWR)

作者： Fattahi, Saeideh Yazdani, Reza Vahidipour, Seyyed Mehdi Univ Kashan Dept Elect & Comp Kashan Iran

ISBN: (纸本)9781728114316

community structure detection in social networks has become a big challenge. Various methods in the literature have been presented to solve this challenge. Recently, several methods have also been proposed to solve this challenge based on a mapping-reduction model, in which data and algorithms are divided between different process nodes so that the complexity of time and memory of community detection in large social networks is reduced. In this paper, a mapping-reduction model is first proposed to detect the structure of communities. Then the proposed framework is rewritten according to a new mechanism called distributed cache memory;distributed cache memory can store different values associated with different keys and, if necessary, put them at different computational nodes. Finally, the proposed rewritten framework has been implemented using SPARK tools and its implementation results have been reported on several major social networks. The performed experiments show the effectiveness of the proposed framework by varying the values of various parameters.

关键词： Social media community structure detection mapping-reduction model SPARK distributed cache memory

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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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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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Hierarchical time-dependent shortest path algorithms for vehicle routing under ITS

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IIE TRANSACTIONS 2016年第2期48卷 158-169页

作者： Nejad, Mark Mahyar Mashayekhy, Lena Chinnam, Ratna Babu Phillips, Anthony Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Wayne State Univ Dept Ind & Syst Engn Detroit MI 48201 USA Ford Motor Co Res & Adv Engn Dearborn MI 48121 USA

The development of efficient algorithms for vehicle routing on time-dependent networks is one of the major challenges in routing under intelligent transportation systems. Existing vehicle routing navigation systems, whether built-in or portable, lack the ability to rely on online servers. Such systems must compute the route in a stand-alone mode with limited hardware processing/memory capacity given an origin/destination pair and departure time. In this article, we propose a computationally efficient, yet effective, hierarchical algorithm to solve the time-dependent shortest path problem. Our proposed algorithm exploits community-based hierarchical representations of road networks, and it recursively reduces the search space in each level of the hierarchy by using our proposed search strategy algorithm. Our proposed algorithm is efficient in terms of finding shortest paths in milliseconds for large-scale road networks while eliminating the need to store preprocessed shortest paths, shortcuts, lower bounds, etc. We demonstrate the performance of the proposed algorithm using data from Detroit, New York, and San Francisco road networks.

关键词： Time-dependent shortest path routing under ITS hierarchical search strategies community structure detection

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Using GPUs to Speed-Up FCM-Based community detection in Social Networks 7

Using GPUs to Speed-Up FCM-Based Community Detection in Soci...

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7th International Conference on Computer Science and Information Technology (CSIT)

作者： Alandoli, Mohammed Shehab, Mohammed Al-Ayyoub, Mahmoud Jararweh, Yaser Al-Smadi, Mohammad Jordan Univ Sci & Technol Irbid Irbid Jordan

ISBN: (纸本)9781467389136

One of the important features of Social Networks (SNs) is community structure detection. Several methods have been proposed to address this problem. One of the interesting methods is based on the famous Fuzzy C-Means (FCM) clustering algorithm. This method consists of three phases: spectral mapping, FCM clustering and modularity computation. Despite being very effective, this method is actually inefficient to deal with large-scale networks. A parallel implementation using GPUs is one of the feasible solutions to address this problem. Hence, this research presents a parallel implementation of FCM and modularity components of the algorithms. The implementation follows the hybrid CPU-GPU approach. We study the many factors affecting the performance speed ups, such as the number of dimensions/features and the network size.

关键词： Social Networks community structure detection Graphic Processing Unit Parallel Computing Fuzzy C-Means

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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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