Abstract
Among the many proposed solutions in graph embedding, traditional random walk-based embedding methods have shown their promise in several fields. However, when the graph contains high-degree nodes, random walks often neglect low- or middle-degree nodes and tend to prefer stepping through high-degree ones instead. This results in random-walk samples providing a very accurate topological representation of neighbourhoods surrounding high-degree nodes, which contrasts with a coarse-grained representation of neighbourhoods surrounding middle and low-degree nodes. This in turn affects the performance of the subsequent predictive models, which tend to overfit high-degree nodes and/or edges having high-degree nodes as one of the vertices. We propose a solution to this problem, which relies on a degree normalization approach. Experiments with popular RW-based embedding methods applied to edge prediction problems involving eight protein-protein interaction (PPI) graphs from the STRING database show the effectiveness of the proposed approach: degree normalization not only improves predictions but also provides more stable results, suggesting that our proposal has a regularization effect leading to a more robust convergence.
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Acknowledgment
This research was supported by the “National Center for Gene Therapy and Drugs based on RNA Technology”, PNRR-NextGenerationEU program [G43C22001320007].
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A Additional Results
A Additional Results
Average (and standard deviation) of the F1 scores of classic (orange bars) versus degree-normalized (blue bars) DeepWalk, Node2Vec, and Walklets across the 10 test (left) and train (right) holdouts (Color figure online)
Average (and standard deviation) of the AUROC scores of classic (orange bars) versus degree-normalized (blue bars) DeepWalk, Node2Vec, and Walklets across the 10 test (left) and train (right) holdouts. (Color figure online)
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Cappelletti, L. et al. (2023). Degree-Normalization Improves Random-Walk-Based Embedding Accuracy in PPI Graphs. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_26
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DOI: https://doi.org/10.1007/978-3-031-34960-7_26
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