A sampling-guided unsupervised learning method to capture percolation in complex networks.

ABSTRACT: The use of machine learning methods in classical and quantum systems has led to novel techniques to classify ordered and disordered phases, as well as uncover transition points in critical phenomena. Efforts to extend these methods to dynamical processes in complex networks is a field of active research. Network-percolation, a measure of resilience and robustness to structural failures, as well as a proxy for spreading processes, has numerous applications in social, technological, and infrastructural systems. A particular challenge is to identify the existence of a percolation cluster in a network in the face of noisy data. Here, we consider bond-percolation, and introduce a sampling approach that leverages the core-periphery structure of such networks at a microscopic scale, using onion d

SUBMITTER: Mimar S 

PROVIDER: S-EPMC8907239 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature