Method for predicting maximum information spreading range on basis of random model

Abstract

The invention belongs to the field of social network modeling and analysis, and particularly relates to a method for predicting the maximum information spreading range on the basis of a random model, and by means of the method, dynamic characteristics of a social network are explored. According to the method, a set of functions capable of describing the network information spreading dynamic characteristics are constructed, a dynamic information spreading model is built according to historical data of social network information spreading, a random model detector is used for predicting the possible maximum information spreading range through a verification and emulation technology, and the node set capable of maximizing the spreading range is found out, wherein information is spread through different node sets. Compared with a traditional spreading range maximization modeling method, the dynamic characteristics of the network can be modeled, so that the initial node set which is predicted out is higher in quality, and the success rate of a network marketing strategy is increased.

Description

technical field [0001] The invention belongs to the field of social network modeling and analysis, and specifically relates to a method for maximizing the spread range of social network information that explores the dynamic characteristics of the social network. This method constructs a set of functions that can describe the dynamics of network information dissemination, establishes a dynamic information dissemination model through the historical data of social network information dissemination, and uses random model detectors to predict through different nodes and node sets through verification and simulation techniques. Propagation, the maximum range that information may spread, and find out the node set that can maximize the spread range. Background technique [0002] In the 1930s, the British anthropologist Radcliffe Brown first used the concept of "social network (social network)" (Social Networks) in his attention to social structure. Over the next 70 years, through t...

AI Technical Summary

Problems solved by technology

In fact, in social networks, hundreds of millions of information dissemination data are accumulated every day, and all information dissemination models are trained based on these data, so with the accumulation of data, the information dissemination model will evolve quickly, so the existing The shortcoming of the method is that it cannot take into account the dynamic nature of social networks

2. The above methods are all modeled based on the relationship between friends in social networks, but these friend relationships cannot reflect the actual information transfer relationship or path. For example, although a large number of nodes have established friend relationships, they have never forwarded information to each other. It is only a weak relationship. In fact, a large number of relationships in the network are weak relationships

Based on the above two main shortcomings, the existing methods obtain solutions with poor quality, cannot find very high-quality initial nodes, and the gap between prediction and reality is very large, which cannot meet actual needs

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