Navegando por Palavras-chave "Modularity"

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    Community detection by consensus genetic-based algorithm for directed networks
    (Wiley-Blackwell, 2016) Mathias, Stefano B. B. R. P. [UNIFESP]; Rosset, Valerio [UNIFESP]; Nascimento, Maria C. V. [UNIFESP]
    Finding communities in networks is a commonly used form of network analysis. There is a myriad of community detection algorithms in the literature to perform this task. In spite of that, the number of community detection algorithms in directed networks is much lower than in undirected networks. However, evaluation measures to estimate the quality of communities in undirected networks nowadays have its adaptation to directed networks as, for example, the well-known modularity measure. This paper introduces a genetic-based consensus clustering to detect communities in directed networks with the directed modularity as the fitness function. Consensus strategies involve combining computational models to improve the quality of solutions generated by a single model. The reason behind the development of a consensus strategy relies on the fact that recent studies indicate that the modularity may fail in detecting expected clusterings. Computational experiments with artificial LFR networks show that the proposed method was very competitive in comparison to existing strategies in the literature. (C) 2016 The Authors. Published by Elsevier B.V.
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    Community detection by modularity maximization using GRASP with path relinking
    (Elsevier B.V., 2013-12-01) Nascimento, Maria C. V. [UNIFESP]; Pitsoulis, Leonidas; Universidade Federal de São Paulo (UNIFESP); Aristotle Univ Thessaloniki
    Complex systems in diverse areas such as biology, sociology and physics are frequently being modelled as graphs, that provide the mathematical framework upon which small scale dynamics between the fundamental elements of the system can reveal large scale system behavior. Community structure in a graph is an important large scale characteristic, and can be described as a natural division of the vertices into densely connected groups, or clusters. Detection of community structure remains up to this date a computationally challenging problem despite the efforts of many researchers from various scientific fields in the past few years. the modularity value of a set of vertex clusters in a graph is a widely used quality measure for community structure, and the relating problem of finding a partition of the vertices into clusters such that the corresponding modularity is maximized is an NP-Hard problem.In this paper we present a Greedy Randomized Adaptive Search Procedure (GRASP) with path relinking, for solving the modularity maximization problem in weighted graphs.A class of (0,1) matrices is introduced that characterizes the family of clusterings in a graph, and a distance function is given that enables us to define an I-neighborhood local search, which generalizes most of the related local search methods that have appeared in the literature. Computational experiments comparing the proposed algorithm with other heuristics from the literature in a set of artificially generated graphs and some well known benchmark instances, indicate that our implementation of GRASP with path relinking consistently produces better quality solutions. (C) 2013 Elsevier B.V. All rights reserved.
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    The crosscutting impact of the AOSD Brazilian research community
    (Elsevier B.V., 2013-04-01) Kulesza, Uira; Soares, Sergio; Chavez, Christina; Castor, Fernando; Borba, Paulo; Lucena, Carlos; Masiero, Paulo; Sant'Anna, Claudio; Ferrari, Fabiano; Alves, Vander; Coelho, Roberta; Figueiredo, Eduardo; Pires, Paulo F.; Delicato, Flavia; Piveta, Eduardo; Silva, Carla; Camargo, Valter; Braga, Rosana; Leite, Julio; Lemos, Otavio; Mendonca, Nabor; Batista, Thais; Bonifacio, Rodrigo; Cacho, Nelio; Silva, Lyrene; von Staa, Arndt; Silveira, Fabio [UNIFESP]; Valente, Marco Tulio; Alencar, Fernanda; Castro, Jaelson; Ramos, Ricardo; Penteado, Rosangela; Rubira, Cecilia; Univ Fed Rio Grande do Norte; Universidade Federal de Pernambuco (UFPE); Universidade Federal da Bahia (UFBA); Pontificia Univ Catolica Rio de Janeiro; Universidade de São Paulo (USP); Universidade Federal de São Carlos (UFSCar); Universidade de Brasília (UnB); Universidade Federal de Minas Gerais (UFMG); Universidade Federal do Rio de Janeiro (UFRJ); Universidade Federal de Sergipe (UFS); Universidade Federal de São Paulo (UNIFESP); Univ Fortaleza; Universidade Estadual de Campinas (UNICAMP)
    Background: Aspect-Oriented Software Development (AOSD) is a paradigm that promotes advanced separation of concerns and modularity throughout the software development lifecycle, with a distinctive emphasis on modular structures that cut across traditional abstraction boundaries. in the last 15 years, research on AOSD has boosted around the world. the AOSD-BR research community (AOSD-BR stands for AOSD in Brazil) emerged in the last decade, and has provided different contributions in a variety of topics. However, despite some evidence in terms of the number and quality of its outcomes, there is no organized characterization of the AOSD-BR community that positions it against the international AOSD Research community and the Software Engineering Research community in Brazil.Aims: in this paper, our main goal is to characterize the AOSD-BR community with respect to the research developed in the last decade, confronting it with the AOSD international community and the Brazilian Software Engineering community.Method: Data collection, validation and analysis were performed in collaboration with several researchers of the AOSD-BR community. the characterization was presented from three different perspectives: (i) a historical timeline of events and main milestones achieved by the community; (ii) an overview of the research developed by the community, in terms of key challenges, open issues and related work; and (iii) an analysis on the impact of the AOSD-BR community outcomes in terms of well-known indicators, such as number of papers and number of citations.Results: Our analysis showed that the AOSD-BR community has impacted both the international AOSD Research community and the Software Engineering Research community in Brazil. (c) 2012 Elsevier Inc. All rights reserved.
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    Detecção de comunidades em redes por algoritmos ensemble
    (Universidade Federal de São Paulo (UNIFESP), 2014-01-12) Mathias, Stefano Bacciuylis Bluyus Rodrigues Pansardis [UNIFESP]; Nascimento, Maria Cristina Vasconcelos Nascimento [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    Many elements in people’s everyday life can be represented by a graph or network. Such a system can possess thousands of elements, many of which can be related to one another. Finding communities or clusters within these systems to evaluate groups of related vertices, as opposed to the entire network, or each individual vertex, appears as a viable alternative for the analysis of these graphs. Due to its applications in various scientific fields, such as Biology, Medicine and Sociology, among others, clustering nodes becomes a viable alternative for these graphs, which may contain a large amount of nodes and edges. For data analysis, and more specifically, data clustering, a common strategy is consensus clustering, which combines two or more computational models to improve the quality of solutions, as compared to those found by only one model. Despite the good results obtained for data clustering, this strategy has not been frequently applied to finding communities in networks. For this reason, this dissertation proposes a genetic algorithm that applies consensus clustering techniques to identify communities in networks. Considering as the objective function a measure here called ”adjusted modularity”, our algorithm performed better than classic algorithms within the scientific literature in experiments performed with artificial and real-world graphs.
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    Solving the lawn mower problem with kaizen programming and lambda-linear genetic programming for module acquisition
    (Associacao Paulista Medicina, 2016) dal Piccol Sotto, Leo Francoso [UNIFESP]; de Melo, Vinicius Veloso [UNIFESP]
    In this work, we have tested a new approach for evolving modular programs: Kaizen Programming (KP) with lambda-Linear Genetic Programming (lambda-LGP) and a heuristic search procedure to solve the well-known Lawn Mower problem. KP is a novel hybrid approach that tries to efficiently combine partial solutions to generate a high-quality complete solution. Being a hybrid, KP may use different types of methods to generate partial solutions, assess their importance to the complete solution, and solve the complete problem. Experiments on the Lawn Mower problem show that the proposed method is effective in finding the expected solution. It is a new alternative for evolving modular programs, but further investigations are necessary to improve its performance.