Group concept-based improved Fast-Newman clustering method applied to complex network

Abstract

The invention discloses a group concept-based improved Fast-Newman clustering method applied to a complex network. According to the invention, the group concept is introduced; the adjacent cluster concept is confined according to the characteristics of complex network cluster structure; a modularity evaluation function proposed by Newman is improved, the maximal modularity evaluation functional value is saved, and the problem that the clustering precision is not highest at global maximum is solved, so the clustering result can more accurately reflect the real network cluster structure. Compared with a conventional FN clustering method, according to the method provided by the invention, the precision of the cluster analysis for the large scale complex network is greatly improved; and especially for the familiar complex network with large size, sparse connection and uneven relation, the clustering effect is more remarkable.

Description

technical field [0001] The invention belongs to the field of data mining of community networks, aims at the clustering of complex network cluster structures, and in particular relates to an optimized clustering method based on group thinking to improve objective functions. Background technique [0002] With the continuous development of disciplines such as computer science, mathematics, physics, biology, sociology, and complexity science, people have discovered that many systems in the real world exist in the form of complex networks, such as the Internet, mobile phone networks, and interactive networks with white paper. net, neuron net, etc. Due to the heterogeneity of nodes and connections in such networks, cluster structure has become one of the most common and important topological properties of complex networks. The network cluster structure has the characteristics that the nodes in the cluster are closely connected to each other and the nodes between the clusters are ...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the defects in the optimization of Q function in the current FN algorithm: the clustering accuracy is not the highest when the Q function reaches the global maximum value, and the clustering results at this time cannot completely and accurately describe the real network cluster structure, and with the complex network As the scale continues to expand, clustering consumes more and more time and resources. The present invention proposes an improved Fast-Newman clustering method based on group thinking for complex networks

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