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Dynamic Information Dissemination Model Incorporating Non-Adjacent Node Interaction

Xinyu Li, Jinyang Huang, Xiang Zhang, Peng Zhao, Meng Wang, Guohang Zhuang, Huan Yan, Xiao Sun, Meng Wang

TL;DR

A novel model, DM-NAI, is proposed, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information dissemination process.

Abstract

Describing the dynamics of information dissemination within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information dissemination. In this paper, we propose a novel model, DM-NAI, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information dissemination process. Extensive experiments are conducted on six datasets to predict the information dissemination range and the dissemination trend of the social network. The experimental results demonstrate an average prediction accuracy range of 94.62% to 96.71%, respectively, significantly outperforming state-of-the-art solutions. This finding illustrates that considering information transmission between non-adjacent users helps DM-NAI achieve more accurate information dissemination predictions.

Dynamic Information Dissemination Model Incorporating Non-Adjacent Node Interaction

TL;DR

A novel model, DM-NAI, is proposed, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information dissemination process.

Abstract

Describing the dynamics of information dissemination within social networks poses a formidable challenge. Despite multiple endeavors aimed at addressing this issue, only a limited number of studies have effectively replicated and forecasted the evolving course of information dissemination. In this paper, we propose a novel model, DM-NAI, which not only considers the information transfer between adjacent users but also takes into account the information transfer between non-adjacent users to comprehensively depict the information dissemination process. Extensive experiments are conducted on six datasets to predict the information dissemination range and the dissemination trend of the social network. The experimental results demonstrate an average prediction accuracy range of 94.62% to 96.71%, respectively, significantly outperforming state-of-the-art solutions. This finding illustrates that considering information transmission between non-adjacent users helps DM-NAI achieve more accurate information dissemination predictions.
Paper Structure (18 sections, 7 equations, 3 figures, 4 tables, 3 algorithms)

This paper contains 18 sections, 7 equations, 3 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. The Proposed Model
  5. Attitude Distribution Similarity
  6. Influence Evaluation between Users
  7. Attitude Change Probability
  8. Attitude Change Mechanism
  9. Algorithm
  10. Information dissemination Process
  11. Node State Change Process
  12. dissemination in Non-adjacent Nodes
  13. Experiment
  14. Dataset
  15. Analysis of dissemination Results
  16. ...and 3 more sections

Figures (3)

  • Figure 1: User Network.
  • Figure 2: The dissemination process from dataset I to dataset VI.
  • Figure 3: The stance change process from dataset I to dataset III.

Theorems & Definitions (5)

  • Definition 1: Activation action
  • Definition 2: Attitude distribution similarity
  • Definition 3: Information dissemination
  • Definition 4: Social influence
  • Definition 5: User attitude's perseverance