OSN community discovery method based on LDA Theme model

Abstract

The invention discloses an online social network (short for OSN) community discovery method based on a Latent Dirichlet Allocation (short for LDA) theme model. The method comprises the following steps first pre-processing data, building an LDA theme model (including an LDA-F model and an LDA-T model) based on a relationship between a user in the online social network and other friends and word information expressed by the user to solve a model probability distribution, then estimating parameters via a Gibbs sampling algorithm, and at last discovering an OSN community according to the estimated parameters. By the use of the OSN community discovery method based on the LDA Theme model, a corresponding probability model can be achieved based on user blog semantic information discovery without the use of information connection via the network topology; blog content semantic similarities are introduced to effectively describe user interest and hobby probability distribution conditions; and with the introduction of community internal topological connection tightness, communities with close internal topological connections can be discovered.

Description

Technical field [0001] The present invention relates to an online social network (Online Social Network, OSN) community discovery mechanism that utilizes an invisible Dirichlet Allocation (Latent Dirichlet Allocation, LDA) theme model, and belongs to the field of social computing, especially the field of community discovery. Background technique [0002] With the rapid development of the Internet, the network has gradually changed from data-centric to human-centric, which has promoted the rapid development of online social networks. Online social networks are different from traditional interpersonal networks. They not only have large-scale users and their friends, but also have a large number of text messages spontaneously expressed by users, which brings new vitality and challenges to community discovery. [0003] The traditional community discovery method is mainly based on connection, that is, the topological structure of the graph. This method divides the community by analyzing...

AI Technical Summary

Problems solved by technology

[0003] The traditional community discovery method is mainly based on the connection, that is, the topological structure of the graph. This method divides the community by analyzing the explicit connection between individuals. The connections between the nodes in the discovered community are relatively close, and the connections between different communities are relatively sparse. , but this method does not take into account the user's theme characteristics

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