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Table 3 Comparison of approaches to module-finding in biological networks

From: Using random walks to identify cancer-associated modules in expression data

Approach Method Heuristic for subgraph search Cluster learning method Cluster overlap Tuning parameters Weighted networks Platform
Walktrap-BM Random walk Differential expression or pairwise similarity Semi-supervised No Modularity, size, score Yes R on Unix, Windows, Mac
jActiveModules[20, 21] Simulated annealing Differential expression (P-values only) Semi-supervised Optional K-modules, number of paths, iterations Yes Cytoscape plugin on Windows, Mac, Linux
Matisse[22] Seed clustering Pairwise similarity and significant seed nodes Semi-supervised Yes Module size, seed number Yes Linux, Windows
NetWalker[23, 31] Random walk Differential expression Unsupervised Optional K-Modules Yes Windows, Mac
Affinity Propagation[66] Seed-based message propagation Pairwise similarity Unsupervised Yes Preference values, seed number Yes Matlab, R on Windows and Linux
MCL[65] Random walk Pairwise similarity Unsupervised No Granularity Yes UNIX platforms