Community detection method based on dynamic synchronous model

Abstract

The invention belongs to the field of network data mining field, and specifically relates to a community detection method based on a dynamic synchronous model. The method comprises the steps of firstly reading social network data, and performing network vectorization according to a social network graph to obtain a vectorized one-dimensional coordinate sequence; setting synchronization parameters and calculating a synchronization range; performing synchronization clustering, wherein each node is synchronized in the synchronization range according to the extensional synchronous model until a local synchronization status is available; dividing communities according to the coordinate position of each node; calculating the modularity of the division; adding the synchronization parameters constantly; executing a new round of synchronization clustering process until the synchronization range covers all the nodes. Nodes in the network are clustered through a kuramoto model, so that a link density can be accurately described, the difference of the network link density is effectively reflected, the automatic detection of a social network community structure is realized, and the community detection results are selected and optimized.

Description

technical field [0001] The invention belongs to the field of network data mining, and in particular relates to a community detection method based on a dynamic synchronization model. Background technique [0002] Data Mining refers to the process of extracting hidden, unknown, and potentially valuable information or patterns from large amounts of data. Clustering is an important analysis technique in the field of data mining, which gathers data into clusters according to the similarity between data in pre-established attributes. The goal of clustering is to divide the finite number of unknown labels into a finite number of discrete data sets. It has no data for learning and training, and only the characteristics of the data points themselves and the similarity between the similarity relationships between the data points can be calculated. Therefore, it is very important to choose an appropriate similarity measurement rule. Commonly used similarity measures include Euclidean...

AI Technical Summary

Problems solved by technology

[0005] The traditional SYN algorithm based on the kuramoto model is not accurate enough to describe the link density, and the numerical range of the calculated structural similarity is narrow, which cannot effectively reflect the difference in network link density

At the same time, when using the kuramoto model for local synchronization, only the synchronization within the ε-neighborhood is considered, and other closely related nodes are not considered

In addition, after the synchronization process, there is no follow-up processing on the micro-communities, resulting in the existence of a large number of micro-communities and making the community detection results inaccurate

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