Community mining method and system based on statistic models

Abstract

The invention belongs to the technical field of networks, and provides a community mining method and device based on statistic models. The method comprises the steps of reading an adjacent matrix A of a signed network N, setting a variation range of a community number to be [Kmin, Kmax], and initializing the community number K to be equal to Kmin, wherein the total number of nodes of the signed network is n, and Kmin and Kmax are integers within the range of n; initializing a statistic model NMK corresponding to each community number K, performing fitting on the statistic models NMK and the signed network N, and calculating a selection standard HK of each statistic model NMK; comparing the selection standards HK of all the statistic models NMK, and selecting the statistic model NMK with the maximum selection standard HK as the optical model NMoptim; according to the optical model NMoptim, determining the community to which each node i belongs in the signed network N, wherein 0<i<=n. According to the community mining method and device based on the statistic models, community mining of the signed network based on the statistic models is achieved, and the signed network community mining accuracy is effectively improved.

Description

technical field [0001] The invention belongs to the field of network technology, and in particular relates to a method and system for mining a community based on a statistical model. Background technique [0002] Compared with the unsigned network, which can only indicate whether there is a relationship between individuals, the symbolic network can expand the single existence relationship into positive and negative relationships. For example, positive links in social networks indicate friendship, liking, and trust, and negative links indicate hostility, dislike, and distrust; positive links in political networks indicate political alliances, and negative links indicate political hostility. These added symbolic information contribute to a deeper understanding of the hidden laws behind the network. As a kind of important structural pattern ubiquitous in complex networks, community is of great significance for understanding the function and development and evolution of network...

AI Technical Summary

Problems solved by technology

However, the above symbolic network community discovery algorithms are all optimization algorithms or heuristic algorithms, and their detection accuracy depends on the quality of the designed optimization objective function or heuristic strategy, and the accuracy of community mining is not high.

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