Information situation awareness and propagation management and control model based on node influence

Abstract

The invention belongs to the field of social network analysis, and discloses an information situation awareness and propagation management and control model based on node influence. The model comprises a data acquisition module, a characteristic extraction module and an information situation awareness and propagation module, wherein the data acquisition module obtains source data from a social network, obtains the personal attribute, the historical behavior and the friend relationship of a node from the source data, and constructs an information propagation network; the characteristic extraction module independently extracts characteristics from two dimensions including a network dynamic attribute and an interaction dynamic attribute, and independently calculates corresponding factor functions; and the information situation awareness and propagation module constructs the information situation awareness and propagation management and control model on the basis of a mean-field theory, simulates an information propagation tendency, perceives an information diffusion tendency, captures the peak hour of information outburst, and mines a dynamic factor which drives the information to be propagated. By use of the method, the dynamics reason of the information propagation in an on-line social network can be effectively explained, an information propagation evolution situation in the social network is perceived, and the method can be widely applied to the relevant field of information propagation.

Description

Technical field [0001] The present invention belongs to the field of social network analysis, and mainly relates to information dissemination in social networks, and specifically aims at node influence driving group interaction and promoting information dissemination for analysis. Background technique [0002] The vigorous development of social networks has provided a rich data basis for relevant research, giving researchers the opportunity to study information dissemination mechanisms, explore information dissemination rules, and obtain phased results based on massive real data. [0003] Among the current information dissemination models for social networks, the more popular ones are 1) the infectious disease model. Among the infectious disease transmission models, the most classic is the SIR model, which divides individuals in the network into three states: infection, susceptibility, and immunity. Each individual's state will continue for a period of time until it is affected by ...

AI Technical Summary

Problems solved by technology

[0005] Problems solved by the present invention: Aiming at how to find highly influential nodes in a multi-attribute social network, the present invention integrates three types of influencing factors in the information dissemination network: the node's own attributes, historical behavior, and network structure, and specifically analyzes these three types of factors Analysis, using the gradient descent algorithm to give different weights to different factors, summed up as internal and external factors that affect the behavior of nodes, and using multiple linear regression methods to build the node's own influence model; for how to calculate the influence between nodes, the present invention Use the shortest path method to find out the nodes in important positions in the network, and focus on some of the most basic characteristics in the social network structure, such as the influence of attributes such as degree distribution, edge betweenness, and node closeness; based on the above basis, the present invention adopts A new SIR model to study the law of information dissemination, the main improvement is to quantify the strength of influence, and provide a theoretical basis for studying the state transition of different node groups in the process of information diffusion

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