Nonlocal PageRank
arXiv:2001.10421 · doi:10.1051/m2an/2020071
Abstract
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 \textit{long-range interaction} between them, does not exhibit concentration phenomena typical of spectral rankings which take 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.
References in corpus (20)
- Community structure in social and biological networks
- Finding community structure in networks using the eigenvectors of matrices
- Higher-order organization of complex networks
- Random Walks on Multiplex Networks
- Emergence of network features from multiplexity
- Localization and centrality in networks
- Designing optimal transport networks
- Long-Range Navigation on Complex Networks using Lévy Random Walks
- A Class of Graph-Geodetic Distances Generalizing the Shortest-Path and the Resistance Distances
- The Spacey Random Walk: a Stochastic Process for Higher-order Data
- Path Laplacian operators and superdiffusive processes on graphs. I. One-dimensional case
- Kleinberg Navigation in Fractal Small World Networks
- Random Multi-Hopper Model. Super-Fast Random Walks on Graphs
- Path Laplacian operators and superdiffusive processes on graphs. II. Two-dimensional lattice
- DNA electrophoresis studied with the cage model
- The graph bottleneck identity
- A framework for second order eigenvector centralities and clustering coefficients
- On the stability of network indices defined by means of matrix functions
- Localization of eigenvector centrality in networks with a cut vertex
- A note on certain ergodicity coefficients