Fusion topology and content community detection method based on deep neural network

Abstract

The invention discloses a fusion topology and content community detection method based on a deep neural network, and belongs to the technical field of complex network analysis. The method comprises the following steps: mining a community structure in a network data set with content information, and respectively modeling topology and content by using modularity maximization and standardized cutting; on the basis of spectral matrix eigenvalue decomposition, matrix low-rank fitting and automatic encoder reconstruction, reconstructing theoretically similar and seamless fusion topology and content so as to construct a community detection model based on an automatic encoder frame deep neural network; and finally, using an evaluation algorithm to normalize the mutual information entropy and the Jaccard coefficient to evaluate the effectiveness of the model. The method has the beneficial effects that the topology and the content are seamlessly fused by utilizing an automatic encoder framework; and on the other hand, the network representation obtained based on the deep neural network has good community detection capability.

Description

technical field [0001] The invention relates to the technical field of complex network analysis, and relates to a deep neural network-based fusion topology and content community detection method. Background technique [0002] There are a large amount of networked data such as social networks and communication networks in the real world, and these data can often be formalized into complex networks. Detecting communities of densely connected vertices in a network is one of the most important tasks in the field of analysis of complex networks. Community detection helps to discover user clusters with similar habits and interests in social networks, and it can also predict the group connection behavior of user clusters in communication networks. Generally, a complex network contains network topology and rich content information, and the content information also has community information, which can be used to improve the accuracy of community detection. However, most of the exis...

AI Technical Summary

Problems solved by technology

However, most of the existing methods are based on manually adjusting the proportion of topology and content information, which is not efficient.

At the same time, deep neural networks have been used in many fields and achieved good results, but few have involved in the field of fusion topology and content community detection

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