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

A technology of complex network and clustering method, applied in the field of optimization class clustering based on group thinking to improve the objective function, can solve the problem that the network cluster structure cannot be completely and accurately described, and achieve the effect of outstanding clustering effect.

Inactive Publication Date: 2012-07-11
河南众诚信息科技股份有限公司
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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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  • Group concept-based improved Fast-Newman clustering method applied to complex network
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[0031] The method of the present invention will be described below in conjunction with the accompanying drawings and specific embodiments.

[0032] As the network scale gradually expands, the probability that nodes in the network have global information gradually decreases. In large-scale complex networks, nodes only have global information with extremely small probability; usually, nodes have local information centered on themselves. In the clustering algorithm, using the knowledge and strategies in the global environment, although the theoretical global optimal solution can be found, the most realistic cluster structure cannot be obtained. Therefore, it is necessary to obtain the real network cluster structure by simulating the range of local information mastered by nodes and the decision-making environment when they are clustered, and searching for the optimal clustering result under this environment.

[0033] The invention proposes and defines the concept of "group" based o...

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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 ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04L29/08
Inventor 童超戴彬牛建伟韩军威
Owner 河南众诚信息科技股份有限公司
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