A Semi-Supervised Community Discovery Method Fused with Node Attributes and Graph Structure

Abstract

A semi-supervised community discovery method that combines node attributes and graph structures belongs to the field of network analysis technology. Including the following steps: 1) Calculate the information entropy of m attributes; 2) Calculate the similarity of attributes; 3) Calculate the similarity of structures using Jaccard similarity; 4) Calculate the total similarity of attributes and structures; 5) Find K initial communities ;6) Initialize the initial community matrix; 7) Combine the semi-supervised method to calculate the community partition matrix; 8) Calculate the reasonable value range of the balance value (trade-off) analysis parameter 9) Obtain the optimal value according to the trade-off and modularity Modularity and community discovery results. The present invention obtains a reasonable division method by continuously adjusting the parameters involved in the algorithm, and finally provides the optimal result for community discovery and the reasonable range of algorithm parameters; fuses attributes for community discovery, and provides a reasonable proportion of attributes Scope, community discovery modularity and compactness are improved.

Description

technical field [0001] The invention belongs to the technical field of network analysis, and in particular relates to a semi-supervised community discovery method which integrates node attributes and graph structures. Background technique [0002] In recent years, machine learning and data mining have become more popular research directions. The learning and mining of the network will be conducive to the rational use of network data. Networks exist in various fields and are widely used. The social relationship between people can be expressed as a social network, the connection of computers is also a computer network, and the interaction between proteins is also a protein network. And how to reasonably analyze and use these network data has become particularly important. Dividing the Weibo user network into communities is beneficial for companies to recommend friends to users. Dividing the protein interaction network (PPI) can effectively identify key proteins, which is a ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a semi-supervised community discovery method that integrates node attributes and graph structures for the problem that existing social network division methods rarely consider node attributes

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