Machine learning of percolation models using graph convolutional neural networks

2022/07/07 by Hua Tian, Tian, Hua, Lirong Zhang +5
Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Opinion Dynamics and Social Influence #Statistical Mechanics (cond-mat.stat-mech)

paper · pdf · doi:10.48550/arxiv.2207.03368

openalex publication_date 2022/07/07 · openalex created_date 2022/07/10 · openalex updated_date 2026/07/28

Abstract

Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and so on. Prediction of percolation thresholds with machine learning methods remains challenging. In this paper, we build a powerful graph convolutional neural network to study the percolation in both supervised and unsupervised ways. From a supervised learning perspective, the graph convolutional neural network simultaneously and correctly trains data of different lattice types, such as the square and triangular lattices. For the unsupervised perspective, combining the graph convolutional neural network and the confusion method, the percolation threshold can be obtained by the "W" shaped performance. The finding of this work opens up the possibility of building a more general framework that can probe the percolation-related phenomenon.

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