A1 Refereed original research article in a scientific journal
Link Prediction with Continuous-Time Classical and Quantum Walks
Authors: Goldsmith Mark, Saarinen Harto, García-Pérez Guillermo, Malmi Joonas, Rossi Matteo AC, Maniscalco Sabrina
Publisher: MDPI
Publication year: 2023
Journal: Entropy
Journal name in source: ENTROPY
Journal acronym: ENTROPY-SWITZ
Article number: 730
Volume: 25
Issue: 5
Number of pages: 15
DOI: https://doi.org/10.3390/e25050730
Web address : https://doi.org/10.3390/e25050730
Self-archived copy’s web address: 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.
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