Table 1 Details of the algorithms used in this study. A description of the strategies implemented by the algorithms and the corresponding references are indicated
From: Exploring the limits of community detection strategies in complex networks
Name of the Algorithm | Strategy used by the algorithm | References |
|---|---|---|
Blondel | Multilevel modularity maximization | |
CNM | Greedy modularity maximization | |
CPM | Multiresolution Potts model | |
DM | Spectral analysis + modularity maximization | |
EO | Modularity maximization | |
HAC | Maximum Likelihood | |
Infomap | Information compression | |
LPA | Label propagation | |
MCL | Simulated flow | |
MSG + VM | Greedy modularity maximization + refinement | |
RB | Multiresolution Potts model | |
RN | Multiresolution Potts model | |
RNSC | Neighborhood tabu search | |
SAVI | Optimal prediction for random walks | |
SCluster | Consensus Hierarchical Clustering + Surprise maximization | |
UVCluster | Consensus Hierarchical Clustering + Surprise maximization | |
Walktrap | Random walks + modularity maximization |
