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Table 3 Comparison of widely-used tasks for modular analysis of networks using the introduced synthetic and real datasets

From: BicNET: Flexible module discovery in large-scale biological networks using biclustering

Approach

Method

Solution aspects and concerns

Efficiency

Clustering (exhaustive and non-overlapping node coverage)

k-Means

Majority of clusters show loose connectedness; High variation on the size of modules (1-to-3 clusters covering almost all nodes and the remaining clusters being statistically non-significant [66])

Efficiency problems for networks with >100.000 interactions

Spectral

Able to isolate modules where the degree of connectedness is approximately constant per module; Only a small subset of clusters is relevant (medium-to-high degree of connectedness)

Medusa implementation only scales for networks with <10.000 interactions

Affinity propagation

The clusters collected from (small samples of) the target biological networks show a generalized lack of biological relevance

Time and memory bottlenecks for small nets (<1000 interactions)

Clustering (non-exhaustive and possibly overlapping node coverage)

CPMw (weighted k-clique percolation)

Intolerance to noise; Intractably large solutions (explosion of similar clusters) with strict coherency criterion (k-clique); Dependence on parameters (e.g. k, intensity level)

Only scales for nets with <5000 nodes (5–10 % density). Bottlenecks for the target biological data even when removing >95 % interactions

Biclustering (bi-sets of nodes)

Hypercliques (unweighted)

Intolerant to missing interactions; Large number of highly similar modules; Dense coherency only

BicNET implementation efficient for large networks (>10000 nodes) with density up to 25 %

Hypercliques (differential)

Intolerant to noise and the prone item-boundaries problem during the selection of differential weights; Dense coherency only

BicNET implementation scales for large dense networks

BicNET (dense assumption)

Focus on dissimilar modules robust to noise and missings, with possibly distinct forms of coherency strength (|L| \(\in\){1,2,3,5})

Efficiency bounded by the search for unweigthed hypercliques (|L|=1)