Network community partitioning method based on immune clone multi-objective optimization

Abstract

The invention discloses a network community partitioning method based on immune clone multi-objective optimization, which mainly solves the problem of instability and low accuracy of the partitioning result of the existing single-objective genetic method. The implementation steps of the network community partitioning method are as follows: (1) reading in a network chart, and generating an adjacent matrix; (2) setting an initialization parameter; (3) calculating a target value matrix; (4) selecting a non-dominated antibody group according to the target value matrix; (5) cloning the non-dominated antibody group; (6) crossing and mutating the cloned non-dominated antibody group to form a parent antibody group; (7) performing local search in the parent antibody group to form an offspring antibody group; (8) obtaining a new parent antibody group according to the size of the target value matrix; and (9) judging whether a running algebra it reaches 50, if so, terminating the running, and outputting the partitioning result of each node in the antibody with maximum target matrix, namely the partitioning result of communities. The partitioning method provided by the invention has the advantages of strong search capability, steady partitioning result and high accuracy.

Description

technical field [0001] The invention belongs to the field of computers, relates to a practical network community division method, and further relates to the network community division in the field of small world network technology, which can be used to divide communities in the network. Background technique [0002] In our lives, network structures exist almost everywhere, and most complex systems are presented in the form of networks, such as metabolic networks, the Internet, email networks, and networks of relatives and friends. A common feature of these networks is that they are a collection of nodes and edges, thus forming a well-known network topology. At the same time, with the continuous popularization of the network and the continuous improvement of application service requirements, the study of complex networks has attracted countless enthusiasts from all over the world, and has become a hot spot of multidisciplinary interdisciplinary research. [0003] At present,...

AI Technical Summary

Problems solved by technology

However, the network community structure division method based on the single-objective genetic algorithm has the following shortcomings: 1. The ability of the genetic algorithm to explore new spaces is limited, and it is easy to converge to a local optimal solution, that is, premature, which is also a genetic algorithm. 2. Because the genetic algorithm is a random algorithm, it needs multiple calculations, the reliability of the result is poor, and the solution cannot be obtained stably.

3. Since there is only one target, only one global or local optimal solution can be obtained in one run

The above three points will lead to the problem of low search ability, unstable division results and low division accuracy when this method solves the community division problem.

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