Social network oriented user relationship analysis method

Abstract

The invention discloses a social network oriented user relationship analysis method, which comprises the following steps: (1) utilizing a web crawler or each social network to open an API (Application Program Interface) platform to obtain original data; (2) extracting the personal attribute of a user, the friend relationship information of the user and the association information of the user, and establishing a personal interest impact factor function, a friend relationship impact factor function and an association-driven impact factor function according to the extracted information; (3) on the basis of a maximum entropy principle, constructing a user relationship analysis module, and carrying out parameter training by the user relationship analysis model to obtain an optimal parameter set; and (4) according to the optimal parameter set and the user relationship analysis module, predicting whether users have a relationship or not. By use of the method, the driving strength of the impact factor can be quantized, and the method also can be used for predicting the development tendency of user relationships and is favorable for finding unknown links and future links in the social network.

Description

technical field [0001] The invention belongs to the field of social network analysis, mainly relates to social network behavior dynamics and a maximum entropy model, and specifically analyzes user relationships in a network structure. Background technique [0002] With the development of mobile Internet technology and web technology, online social network has become an important tool for people's daily communication, entertainment and communication. The relationship between users in the network is the foundation of online social networks, which greatly affects the formation and development of online social networks. Therefore, it is particularly important to analyze the factors that affect user relationships. [0003] At this stage, there are different aspects of user relationship analysis, the most important of which is the research on user relationship prediction. In similarity-based user relationship prediction, it is generally believed that the higher the similarity bet...

AI Technical Summary

Problems solved by technology

In view of the difficulty of quantifying various influencing factors and the uncertain weight distribution, considering the advantages of the maximum entropy model that does not need to rely on the correlation between features when selecting features, a user relationship analysis model is constructed based on the principle of maximum entropy. Quantify the driving strength of various factors on the establishment of user relationships, further excavate the key factors that affect link establishment, and then analyze user relationships

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