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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Enhanced graph coevolution network for social network analysis using assimilation modified emotional algorithm.

Hsiao-Hui Li¹, Po-Chun Chang², Yuan-Hsun Liao³

¹Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology, Cijin Campus, Gaoxiong, 805301, Taiwan.

Scientific Reports

|March 2, 2026

Summary

The new Assimilation Modified Emotional (AME) algorithm improves social network analysis by simulating emotional transitions. This AI approach enhances emotional feature extraction accuracy over traditional methods.

Keywords:

Graph machine learning Label propagation algorithm Social network analysis

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Area of Science:

Artificial Intelligence
Social Network Analysis
Computational Social Science

Background:

Traditional label propagation algorithms (LPA) lack emotional representation and are limited by local dependencies.
Existing methods like asynchronous label propagation and the Louvain algorithm do not capture dynamic emotional states effectively.

Purpose of the Study:

To introduce the Assimilation Modified Emotional (AME) algorithm, an enhancement of LPA for social network analysis.
To address limitations in emotional feature extraction and dynamic emotional state simulation.
To improve the accuracy and performance of AI models in social network contexts.

Main Methods:

The AME algorithm integrates spectral algorithms, Markov chains, graph coarsening, and link prediction.
It simulates and optimizes emotional transitions within social networks.
Multi-label encoding is employed to enhance label representation for dynamic emotional states.

Main Results:

The AME algorithm demonstrated superior performance compared to traditional LPA methods.
Improvements were observed in both accuracy and loss values.
The enhanced approach effectively simulates dynamic emotional states.

Conclusions:

The AME algorithm offers a significant advancement for AI in social network analysis and emotional feature extraction.
It provides a more robust method for understanding and modeling emotional dynamics in networks.
The findings suggest strong potential for practical applications in AI-driven social media analysis.