Associate Professor (Reader) in Machine Learning
School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
School of Mathematics, Gran Sasso Science Institute
JCMB, King’s Buildings, Edinburgh EH93FD UK
email: f dot tudisco at ed.ac.uk
In this work we introduce and study a nonlocal version of the PageRank. In our approach, the random walker explores the graph using longer excursions than just moving between neighboring nodes. As a result, the corresponding ranking of the nodes, which takes into account a long-range interaction between them, does not exhibit concentration phenomena typical for spectral rankings taking into account just local interactions. We show that the predictive value of the rankings obtained using our proposals is considerably improved on different real world problems.
@article{cipolla2021nonlocal,
title={Nonlocal PageRank},
author={Cipolla, Stefano and Durastante, Fabio and Tudisco, Francesco},
journal={ESAIM Mathematical Modelling and Numerical Analysis},
pages={77--97},
volume={55},
year={2021}
}
Keywords: networks Perron-Frobenius theory Markov chain nonlocal dynamics