Link Prediction with Continuous-Time Classical and Quantum Walks



: Goldsmith Mark, Saarinen Harto, García-Pérez Guillermo, Malmi Joonas, Rossi Matteo AC, Maniscalco Sabrina

Publisher: MDPI

: 2023

: Entropy

: ENTROPY

: ENTROPY-SWITZ

:  730

: 25

: 5

: 15

DOI: https://doi.org/10.3390/e25050730

: https://doi.org/10.3390/e25050730

: https://research.utu.fi/converis/portal/detail/Publication/179868462


Protein-protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism, and they form the basis for the field of network medicine. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for specifying the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on six real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state-of-the-art.

Last updated on 2024-26-11 at 12:24