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Echo wall social network structure prediction method based on compressed sensing

A network structure and compressed sensing technology, applied in the field of text recognition, can solve problems such as lack of

Inactive Publication Date: 2021-09-28
UNIV OF SHANGHAI FOR SCI & TECH
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

The existing compressed sensing technology is mainly in the field of user image processing. In the structure prediction of social networks, it is necessary to redesign the reconstruction prediction method suitable for the echo gallery effect. The echo gallery is a kind of influence related to user opinions that exists widely in social networks. phenomenon, but there is a lack of predictive methods that can reconfigure the network

Method used

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  • Echo wall social network structure prediction method based on compressed sensing
  • Echo wall social network structure prediction method based on compressed sensing
  • Echo wall social network structure prediction method based on compressed sensing

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Embodiment 1

[0025] Step S1 HK model. The Hegselmann-Krause continuous opinion dynamics model (HK model) assumes that there are $n$ and two polarized opinions P and Q in the network, and opinions take values ​​in a continuous interval. Each user i∈Rn has a continuous opinion xi, xi∈(0,1]. xi=0 means that user i supports opinion P, and xi=1 means that user i supports opinion Q. User x_i’s opinion is in [xi-ε, xi The interval of +ε]$ is affected by neighbors. The bounded confidence parameter ε is the key consensus threshold of the neighbors’ approach opinions that users intend to adopt. The discrete-time HK model can be described as

[0026]

[0027] Step S2 SBM model. A Stochastic Block Model (SBM) is used to generate the community structure. The generative network of SBM depends on the node connection probability given by the community relations. Nodes in the same community are more closely connected than nodes in another community. Considering the number k of communities, the prior...

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Abstract

The invention discloses an echo wall social network structure prediction method based on compressed sensing, and the method comprises the following steps: S1, generating an observation signal through a Hegselmann-Krause continuous viewpoint dynamics model (HK model), which comprises the steps: generating a continuous time sequence signal for each node in a network, and enabling the value range of the signal to be [0, 1]; s2, forming a network structure through a Stochastic BlockModel community structure generation model (SBM model), wherein the network structure in the step S1 is generated in the SBM model, and each node belongs to one community according to the SBM model; s3, constructing viewpoint distribution of an echo chamber effect and Chaos chamber effect viewpoint distribution of comparison, and according to the node attributes in the step S1 and the network structure attributes in the step S2, distributing the node attributes into the network structure according to the echo wall effect and the random effect; and S4, constructing a network structure reconstruction convex optimization algorithm based on compressed sensing, and performing network structure reconstruction one by one for the networks with attribute structures from S1 to S3. According to the invention, the social network structure is predicted through the restoration construction of the compressed sensing in the echo wall social network.

Description

technical field [0001] The invention relates to the technical field of text recognition, in particular to a method for predicting the structure of an echo gallery social network based on compressed sensing. Background technique [0002] The echo chamber effect in social networks describes a situation where certain ideas, beliefs, or data points are reinforced through repetition in a closed system that does not allow alternative or competing ideas or concepts to flow freely. In an echo chamber, there is an implication that certain ideas or outcomes win out because of an inherent inequity in how the input is collected. Some data are often selected from general sources on social networking platforms based on heuristic or learning algorithms. Users may see messages on social media become an "echo chamber" of common, similar thoughts and wonder why this is happening. However, for the echoing gallery effect, there is a lack of an effective social network structure prediction met...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06Q10/04G06N20/00
CPCG06Q50/01G06Q10/04G06N20/00
Inventor 李仁德郭强刘建国
Owner UNIV OF SHANGHAI FOR SCI & TECH
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