Complex network sampling method for keeping community structure

Abstract

The invention provides a complex network sampling method for keeping a community structure. The complex network sampling method is suitable for data sampling for large-scale data analysis under the limitation of existing hardware conditions. Two concepts of community dimensionality and community center are provided, community dimensionalities of all of nodes in a network are first obtained, then community centers are selected in the order of smallest to largest in community dimensionality, sampling is performed based on a forest fire sampling mode, the sampling size is determined according to the proportion of the community dimensionalities of the community centers to the community dimensionalities of the remaining community centers, and all of sampling nodes are output after all of community centers are sampled. An experiment proves that by means of the complex network sampling method, a sampling result is similar to data of an original network, the community structure is kept well, data scale is reduced to a great degree, and accordingly convenience is provided for big data processing under the limitation of existing hardware conditions.

Description

technical field [0001] The invention proposes a complex network sampling method for maintaining community structure, which belongs to the field of computer application and complex network. Background technique [0002] In recent years, it has been discovered that many systems in the real world exist in the form of complex networks, such as social networks, the Internet, mobile phone networks, protein interaction networks, and neuronal networks. Complex networks have the characteristics of huge number of nodes and edges, complex network structure, diversity of nodes and edges, evolutionary evolution and dynamic complexity. For example, the World Wide Web currently has more than a trillion Uniform Resource Locators (URLs), Facebook has 1 billion user nodes and 100 billion user relationship connections, the brain neuron network has tens of billions of nodes, and the mobile communication networks of the three major operators in China Both have hundreds of millions of users. Ho...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem of difficult large-scale and complex network data processing, the present invention proposes a complex network sampling method that maintains the community structure. On the premise that the community structure of the original network can be better ensured, the data scale is greatly reduced. Network data are preprocessed to facilitate subsequent community structure research

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