Topic participation prediction method based on triadic group in social network

Abstract

The invention provides a user topic participation prediction method, and belongs to the field of data mining and information retrieval. A data acquisition module acquires user information under a hot topic; a feature extraction module finds out an information triadic group formed by users participating in the topic of each time period by performing time slicing on the behavior of topic participation of the users, extracts feature properties for each user and extracts the properties of the information triadic group based on the properties of the users; a model training module performs modeling of the closing behavior of the information triadic group based on the properties of the information triadic group to construct a triadic information factor graph model and finds out the closed information triadic groups in the next stage of the hot topic; and a result prediction module predicts the users participating in the topic according to the predicted closing result of the information triadic groups. According to the method, the behavior of the users of participating in the topic is regarded as the closing behavior of the information triadic group so that a new idea is provided for topic participation prediction in the social network, and the method can be widely applied to the related fields of topic recommendation and topic analysis and the like.

Description

technical field [0001] The invention relates to social network information analysis technology, in particular to the fields of information dissemination and topic analysis in social networks. Background technique [0002] A social network refers to a collection of social participants and their relationships. It can also be said that a social network is a collection of nodes (social participants) and edges between nodes (relationships between social participants). Therefore, graphical models are often used to describe such structures. Typical social network research areas include role recognition, topic analysis, and information dissemination. Among them, the field of topic analysis has become one of the hot research directions in recent years because it can understand the direction of public opinion and obtain effective information in a timely manner. [0003] The current topic analysis research mainly includes topic discovery, topic participation prediction, etc. The meth...

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Problems solved by technology

This paper shows that the triplet analysis method can effectively analyze the establishment of user relationships, but the triplet structure is currently only used in the field of link prediction, and the traditional triplet factor graph model is not suitable for directly predicting user participation topics The problem

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