Method for social network association excavation based on multi-scale geographic information

Abstract

The invention relates to a method for social network association excavation based on multi-scale geographic information. The method comprises the steps that (1) user sign-in data is acquired, pre-processing is carried out to the data, and structured data is obtained; (2) different methods for map division as well as scale standards for each method are set, and multiple corresponding position IDs are computed and acquired according to GPS data in the user sign-in data; (3) weights of the different positions are computed and acquired according to the quantities of sign-in users, and contributions made by the different positions for social relation prediction are represented; (4) characteristic extraction is carried out according to weight information of the positions, and position interaction characteristics of all the users can be obtained; (5) a classifier is trained by the extracted characteristics, and a relation prediction model is obtained; and (6) the obtained relation prediction model is used to predict a target user, and a prediction result of social network relations can be obtained. The method provided by the invention is characterized in that the position sign-in information is fully used to implement training and obtain the prediction model with higher robustness, and the ideal and stable prediction result can be obtained.

Description

technical field [0001] The invention belongs to the fields of information technology and social network technology, and in particular relates to a social network association mining method based on multi-scale geographic information. Background technique [0002] In the field of social network research, social network association mining is an important research direction, and it is the basis of many other studies such as community discovery and recommendation systems. For example, people tend to buy products recommended by relatives and friends, and communities are often composed of people who are familiar with each other. Therefore, social network association mining has become a hot topic in the field of social network research and has attracted extensive attention. [0003] In the traditional sense, social network association mining often uses the method of graph model to make predictions, that is, the social network association mining network is abstracted into a graph mo...

AI Technical Summary

Problems solved by technology

However, if two people are detected at the same time in a park or library, it is difficult to infer whether there is a social network connection between the two

[0007] c. Insufficient handling of data skew

At present, there are few studies on models that use location information to predict user relationships, especially in relationship prediction, there are fewer users with social network connections, resulting in too small a proportion of positive samples when training the prediction model, resulting in weak robustness of the prediction model

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