Interest and network structure double-cohesion social network community discovering method

Abstract

The invention discloses an interest and network structure double-cohesion social network community discovering method which comprises the steps of: firstly, archiving content issued by users in a social network, extracting interest characteristics of each user by using an existing interest characteristic extraction method, and further obtaining interest characteristic collection of each user relationship by adopting intersection operation to form a social network R-C model; on the basis, calculating interest characteristic similarity of two user relationships having two common users by adopting an existing similarity calculation method; then, forming a social network weighted undirected graph by regarding a user relationship in the R-C model as a node, regarding whether a common friend exists between two user relationships as a border, and regarding the interest characteristic similarity among the user relationships as a weight value of the border; then, excavating user relationship community by adopting an existing weighted undirected network community discovering algorithm; finally, mapping the user relationship in the user relationship community into associated users directly to form a social network user community.

Description

technical field [0001] The invention relates to the field of intelligent information processing and data mining, in particular to a method for mining a community with double cohesion of interest and network structure on a social network. Background technique [0002] Community detection refers to the discovery of cohesive subgroups in social networks. Community discovery is an important issue in social network analysis. It helps people to further recognize, understand and master the complex network objects studied, and then realize more in-depth application research, such as personalized recommendation, friend recommendation, and large-scale network compression solution , heterogeneous network analysis, social network evolution, etc. The discovery of user communities with dual cohesion of interest and network structure is an important research content for precise marketing and accurate personalized recommendation services. In real life, people often disseminate the informa...

AI Technical Summary

Problems solved by technology

[0015] In summary, the current social network user community discovery methods have the following deficiencies: ① The algorithm is not considered comprehensive; ② The algorithm efficiency is low; ③ The LCA algorithm does not consider the real interest characteristics of edges

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