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Research on Intelligent Algorithms for Network Community Mining

Author: HeDongXiao
Tutor: ZhouChunGuang
School: Jilin University
Course: Applied Computer Technology
Keywords: Complex networks Community structure Genetic Algorithms Integrated learning Local search
CLC: O157.5
Type: Master's thesis
Year: 2010
Downloads: 179
Quote: 1
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Abstract


The complex network community mining is an emerging research direction , its complex network topology analysis , functional analysis and behavior prediction has important theoretical significance and practical value , thus increasing concern of scientists . Complex network community mining depth study for the shortcomings of the current genetic algorithm is difficult to apply complex network community mining , the paper proposes a genetic algorithm based on integrated learning for complex network community mining . The algorithm will be integrated learning introduced into the crossover operator , parent individual clustering information is supplemented by local information of the network topology to generate new individuals avoid the traditional crossover operator simply exchange the character block and ignore the clustering content problems. For the integration of learning into full play , the paper puts forward the initial population generated based on the Markov random walk algorithm , the initial population generation algorithm is able to produce some clustering precision and strong diversity initial individual. The proposed initial population generation algorithm based on the crossover operator of integrated learning complement each other to enhance the algorithm optimization ability . In addition, the algorithm local search mechanism for the mutation operator through the forced variability node from most of its neighbors in the same community , targeted to narrow the search space , thereby speeding up the convergence rate of the algorithm . Tested in the computer-generated network and the network of five widely used real-world community representative and current mining algorithms to compare experimental results show the feasibility and effectiveness of the algorithm .

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CLC: > Mathematical sciences and chemical > Mathematics > Algebra,number theory, portfolio theory > Combinatorics ( combinatorics ) > Graph Theory
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