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Optimizing network propagation for multi-omics data integration
Fig 1
Normalized Laplacian induces a topology bias.
Distributions of log-transformed node scores after network propagation using the normalized Laplacian (
and t = 0.7 for Heat Diffusion, and 
and α = 0.5 for Random Walk with Restart). The input vector was a unit vector, i.e. all nodes had identical initial scores. Hub nodes (top 10% nodes with the highest degree) gain higher average scores, whereas non-hub nodes (bottom 10% nodes with the lowest degree) get lower average scores (Two-sided Wilcoxon rank sum test p-value < 2.2∙10−16 (***)).
doi: https://doi.org/10.1371/journal.pcbi.1009161.g001