Social network information propagation historical ordering method based on causal inference

Abstract

The invention discloses a social network information propagation historical ordering method based on causal inference, and the method comprises the steps: collecting the historical record of information propagation of a social network, and ordering the nodes or edges in the historical record of information propagation according to the contribution degrees of the nodes or edges in the information propagation. According to the invention, the method is proposed for the first time, obtains the necessary part and a sufficient part of a causal effect through employing two indexes: responsibility and capability, avoids a complex algorithm, carries out the ordering of the nodes or edges in the historical record of information propagation according to the contribution degrees of the nodes or edges in the information propagation, enables an algorithm for calculating the contribution degree of each participant in the information propagation of the social network to be simpler, and improves the user's understanding of the contribution of each participant.

Description

Technical field [0001] The invention relates to the technical field of computer social networks, in particular to a method for sorting social network information dissemination history based on causal inference. Background technique [0002] In modern life, online social networks (SNS) have deeply affected everyone’s daily life. A large amount of social information appears and spread on social networking sites. Usually, a user will get the same information through different followers or friends. information. In social networks, the process of information dissemination records its dissemination path through dissemination history records. People can use dissemination history records to explain user behavior in social networks. However, these dissemination history records can easily become very complex and large in scale. Huge, which limits the intuitive understanding of users. [0003] In order to reduce this kind of information overload, it is urgent for those skilled in the art to...

AI Technical Summary

Problems solved by technology

In social networks, the information dissemination process is recorded through the dissemination history records, and people can use the dissemination history records to explain the user's behavior in the social network. However, these dissemination history records can easily become very complex and large-scale Huge, thus limiting the user's intuitive understanding of it

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