A label propagation natural heuristic-based dynamic network community structure identification method

Abstract

The invention relates to a label propagation natural heuristic-based dynamic network community structure identification method, and belongs to the field of artificial intelligence and complex networks. According to the invention, the label propagation algorithm is used for initializing network communities and restricting the conditions of the variation process, so that the detection efficiency andeffectiveness can be further improved; Through genetic manipulation, diversity is increased on the premise that a community structure is maintained; And a particle swarm algorithm is used to avoid falling into local optimum, and a global optimum solution is obtained. And finally, according to the experimental results of testing on the artificial network and the real network, the node tags can beupdated according to the node degrees through the improvement of the tag propagation algorithm. And the nodes with high degrees have greater influence on surrounding nodes, so that the problem that the iteration result is unstable is solved. And one-time synchronous updating operation is carried out by using the abrupt change operation and the particles with the random numbers smaller than the abrupt change rate. Not only is a good community structure maintained, but also diversity of particle positions is increased, and local optimum is avoided.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and complex networks, and relates to a dynamic network community structure identification method based on label propagation natural heuristic. Background technique [0002] The network can describe the complex relationship between the real world. For example, the nodes in the network represent the entities in the real world, and the edges represent the relationship between entities. Complex networks are an effective method for modeling real-world networks. Complex network is not only a representation of data, but also a means of scientific research. Therefore, complex network is currently receiving extensive attention and research. Among them, the identification of network community structure is one of the most important features in complex networks, and it is an important method to understand the structure and function of the entire network. Moreover, the application of community structur...

AI Technical Summary

Problems solved by technology

1. The research on incremental clustering method mainly regards incremental data as time series data and data in a specific order: the first one is to iteratively calculate the network at each moment, but does not use the previous clustering data. As a result, its advantage is high precision, but its disadvantage is that the calculation cost is too large; the other is to use the result of the last clustering to divide the nodes into existing communities, and its advantage is to make full use of the last clustering result to calculate The overhead is small, but the disadvantage is that it is not sensitive to changes in the network topology, resulting in low accuracy

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