Social network evolution analytical method and system for implementing evolution point discovery

Abstract

The invention provides a social network evolution analytical method and a social network evolution analytical system for implementing evolution point discovery. The method comprises the following steps: calculating the similarity between two adjacent social networks according to a time sequence; solving a brake point of the similarity according to the calculation result, namely an evolution point; selecting superposition to the social network snapshot between the adjacent brake points to form a social network topological graph in a corresponding time period, wherein, the average distance between the social network topological graph in the corresponding time period and the adjacent brake points is smallest; the social network snapshot is a series of social networks divided according to thesmallest particle size; and clustering and performing correlation analysis according to the social network topological graph. By solving the brake point of the social network similarity, the invention captures the evolution point generating great changes to the development of things in a social network evolution process, and performs topology analysis on the social network in the time period before and after the evolution point, thus accurately and rapidly discovering event occurrence in the network evolution process, disclosing the influence on the network evolution by the events, and improving accurate analytical result.

Description

technical field [0001] The invention relates to a social network evolution analysis method and system for realizing evolution point discovery. 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 ...

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 refer to abnormal connections related to the subject of social network analysis, such as people's calls during the Spring Festival

Incremental analysis methods, on the one hand, may amplify noise during the analysis process, or often fail to capture the evolution point (ie event) that has a significant change in the development of things during the time period, thus failing to provide accurate analysis results

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