Online community partitioning method based on genetic algorithm and priori knowledge

Abstract

The invention discloses an online community partitioning method based on genetic algorithm and priori knowledge, mainly solving the problems in the prior art, such as poor portioning stability, low accuracy rate and low efficiency. The online community partitioning method comprises the steps of: 1, reading an actual network diagram in to generate an adjacent matrix; 2, setting initialization parameters; 3, randomly generating an initial population; 4, calculating a fitness value according to the adjacent matrix, selecting 450 chromosomes with the maximum fitness values as a parent population; 5, carrying out genetic manipulation on the chromosomes in the parent population, forming a child population; 6, partially searching the child population; 7, merging the parent population with the child population to obtain a new parent population; and 8, judging whether an operation algebra in the step 3 reaches 50, is so, ending the operation, and outputting partitioning results of all nodes in the chromosomes with the maximum fitness, namely community partitioning results. The online community partitioning method has the advantages of stable partitioning result, high partitioning accuracy rate and high partitioning efficiency.

Description

technical field [0001] The invention belongs to the field of computers, relates to a community division method in a complex network, and further relates to the community division of a WS small-world network model, which can be used to divide communities in a network, and is one of the research hotspots in complex networks in recent years. Background technique [0002] At present, complex networks have become a research hotspot in many disciplines. With the in-depth research on the physical meaning and mathematical characteristics of network properties, people have found that many actual networks have community structures, that is, the entire network is composed of several "communities". of. The connections between nodes within each community are relatively close, but the connections between communities are relatively sparse. Revealing the network community structure is of great significance for in-depth understanding of network structure and analysis of network characterist...

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Problems solved by technology

[0006] At present, the network community structure division method based on genetic algorithm is mainly composed of initialization phase, crossover operator and mutation operator. The most important thing is to mutate according to the neighborhood node information. The disadvantages of this method are: random initialization leads to low efficiency of algorithm initialization, one-way crossover makes the diversity of the obtained solutions poor, and based on neighborhood node information. The way of mutation makes the algorithm division efficiency low, which leads to weak global search ability and local search ability of the algorithm, and low division accuracy and division efficiency

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