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Link Prediction with Continuous-Time Classical and Quantum Walks
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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15
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Entropy, Volume 25, issue 5, pp. 1-15
Abstract
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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Funding Information: M.G., H.S., S.M., and G.G.-P. acknowledge support from the Emmy.network foundation. S.M. and M.A.C.R. acknowledge financial support from the Academy of Finland via the Centre of Excellence program (Project No. 336810 and Project No. 336814). G.G.-P. acknowledges financial support from the Academy of Finland via the Postdoctoral Researcher program (Project No. 341985). Publisher Copyright: © 2023 by the authors.
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Goldsmith, M, Saarinen, H, García-Pérez, G, Malmi, J, Rossi, M A C & Maniscalco, S 2023, 'Link Prediction with Continuous-Time Classical and Quantum Walks', Entropy, vol. 25, no. 5, 730, pp. 1-15. https://doi.org/10.3390/e25050730