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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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Dual-learning Multi-hop Nonnegative Matrix Factorization for community detection.

Xu Bai¹, Bilian Chen¹, Zhijian Zhuo¹

¹Department of Automation, School of Aerospace Engineering, Xiamen University, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making, Xiamen, 361005, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 14, 2024

Summary

This summary is machine-generated.

This study introduces Dual-learning Multi-hop NMF (DL-MHNMF) for network community detection. The novel method improves accuracy by leveraging multi-hop information and shared results across network hops.

Keywords:

Community detection Dual-learning Multiview clustering Nonnegative matrix factorization (NMF)Optimization

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Area of Science:

Network Science
Data Mining
Machine Learning

Background:

Community detection is crucial in network science.
Nonnegative matrix factorization (NMF) is a popular method.
Existing NMF methods often ignore multi-hop network information.

Purpose of the Study:

To propose a novel NMF-based community detection method considering multi-hop information.
To develop a method that integrates shared and hop-specific community detection results.
To enhance the accuracy of community detection in complex networks.

Main Methods:

Dual-learning Multi-hop NMF (DL-MHNMF) algorithm.
Iterative optimization with guaranteed convergence.
Methodology involves iteratively removing specific results to refine shared outcomes.

Main Results:

DL-MHNMF effectively utilizes multi-hop connectivity and shared results.
The proposed method achieves enhanced detection accuracy.
Experimental validation on eleven datasets shows superior performance compared to fourteen other algorithms.

Conclusions:

DL-MHNMF represents a significant advancement in NMF-based community detection.
The method's ability to incorporate multi-hop information and dual-learning principles leads to improved accuracy.
The findings suggest DL-MHNMF is a state-of-the-art approach for network community detection.