Social network clustering correlation analysis method and system based on core point

Abstract

The invention provides social network clustering correlation analysis method and system based on a core point. The method comprises the following steps of: acquiring a stable time period of a social network; carrying out approximation on the social network at the stable time period to obtain a social network approximation graph; solving maximal cliques in the social network approximation graph; carrying out merging on the maximal cliques to obtain leagues according to the proportion of common points among the maximal cliques in the corresponding maximal cliques; and correlating leagues at different moments according to the similarity. In the invention, the approximation is carried out on the social network at the acquired stable time period, and the approximation method can effectively reduce the influence of noise on the subsequent analysis, simultaneously retains the basic features of the social network and ensures that the analysis result is more accurate. In the process of discovering the leagues, the invention directly carries out the merging on the maximal cliques, can rapidly discover the leagues and further rapidly acquire the analysis result.

Description

technical field [0001] The invention relates to a core point-based social network clustering association analysis method and system. Background technique [0002] At present, the objects processed by data mining tasks are mainly individual data instances, which can often be represented by a vector containing multiple attribute values, and these data instances are assumed to be statistically independent. For example, to train a disease diagnosis system, its task is to diagnose whether a subject suffers from a certain infectious disease, the usual practice is to use a vector to represent a subject, and at the same time assume that the relationship between the subjects The disease conditions are independent of each other, that is, knowing a confirmed patient does not provide any help in diagnosing whether other subjects have the disease. Intuitive experience tells us that this assumption is unreasonable. If a person's relatives and friends suffer from the infectious disease, h...

AI Technical Summary

Problems solved by technology

However, in the actual situation, the occurrence and development of things are not uniform, and the incremental analysis method cannot accurately analyze the noise and events in the social network. Among them, the noise refers to the connection that has nothing to do with the subject of social network analysis, mainly caused by the social network. It is caused by the randomness and uncertainty of individual behaviors of the characteristics, such as invalid calls caused by dialing the wrong phone number; events

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