Optimizing network propagation for multi-omics data integration
Table 1
Different graph normalization approaches and their impact on propagated scores.
Network propagation leads to topology bias when the normalized Laplacian of the graph is used, whereas the degree row-normalized adjacency matrix does not lead to bias on the hub nodes. The Laplacian of the graph cannot be used for RWR because the iterative process is not guaranteed to converge for all α’s. Yes: presence of topology bias, No: absence of topology bias for the respective combination of propagation algorithm and graph normalization approach. The symbol “-” indicates that convergence is not guaranteed for all values of the smoothing parameter.