Method and equipment for predicting information propagation in social network

Abstract

The embodiment of the invention provides a method for predicting information propagation in a social network. The method comprises the steps that to-be-predicted target information is acquired, and influences of K clusters are acquired, wherein the target information is released or transmitted by a first user at a first moment, and K is a positive integer; role probability distribution of the first user is determined, and a second user who does not propagate the target information is determined, wherein the role probability distribution of the first user is used to represent probabilities of belongingness of the first user to the K clusters; and according to the influences of the K clusters and the role probability distribution of the first user, the probability for the second user to transmit the target information from the first user can be determined. According to the embodiment of the invention, the influences of the K clusters can be used to predict propagation of the target information in the social network; and the prediction method requires a small computation quantity, has high computation efficiency and can solve a cold start problem aiming at a new user.

Description

technical field [0001] Embodiments of the present invention relate to the field of information processing, and more specifically, to a method and device for predicting information dissemination in a social network. Background technique [0002] The information dissemination model calculates the help or influence of different factors on the dissemination of information through the dissemination records of different information on the network, including path, range and / or speed, and then makes predictions on the dissemination path of new or existing information. Among them, the most important factor of information dissemination is the influence of different network nodes on the information dissemination process, and it is widely used in different types of networks such as social networks, communication networks, computer networks, and the Internet. On social networks, one of the most important applications of the information dissemination model is to find the most valuable use...

AI Technical Summary

Problems solved by technology

However, the information dissemination model needs to give or learn the weights of the edges between all users first.

For a social network with huge user data, the complexity of learning the weights of edges between all users is high, which leads to low computational efficiency of the information dissemination model

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