Algorithms’ Table¶
In the following table you can find an up-to-date list of the Community Detection algorithms made available within cdlib
.
Algorithms are listed in alphabetical order along with:
- a few additional information on the graph typologies they handle, and
- the main expected characteristics of the clustering they produce,
- (when available) the theoretical computational complexity as estimated by their authors.
All algorithms are assumed - apart few, reported, exceptions - to work on undirected and unweighted graphs.
Complexity notation. When discussing the time complexity the following notation is assumed:
- n: number of nodes
- m: number of edges
- k: number of iterations
- c: number of communities
- d: average node degree
Algorithm | Network | Communities | Complexity | ||||||||
Directed | Weighted | Bipartite | Feature-Rich | Temporal | Crisp | Overlaps | Nested | Fuzzy | Hierarchical | Time | |
agdl | x | x | x | O(n^2) | |||||||
angel | x | O(n) | |||||||||
aslpaw | x | O(kn) | |||||||||
async_fluid | x | O(m) | |||||||||
belief | x | O(kn) | |||||||||
big_clam | x | x | x | O(n) | |||||||
bimlpa | x | x | O(m) | ||||||||
chinesewhispers | x | x | O(km) | ||||||||
condor | x | x | |||||||||
conga | x | ||||||||||
congo | x | O(nm^2) | |||||||||
core_expansion | x | O(nlogn) | |||||||||
cpm | x | x | |||||||||
CPM_bipartite | x | x | |||||||||
coach | x | ||||||||||
danmf | x | x | |||||||||
dcs | x | x | |||||||||
demon | x | ||||||||||
der | x | x | |||||||||
dpclus | x | x | |||||||||
edmot | x | x | x | x | |||||||
ebgc | x | ||||||||||
ego_networks | x | O(m) | |||||||||
egonet_splitter | x | O(m^3/2 ) | |||||||||
eigenvector | x | ||||||||||
em | x | x | x | x | |||||||
endntm | x | ||||||||||
eva | x | x | |||||||||
frc_fgsn | x | x | x | ||||||||
ga | x | ||||||||||
gdmp2 | x | x | x | ||||||||
gemsec | x | x | |||||||||
girvan_newman | x | x | |||||||||
graph_entropy | x | x | |||||||||
greedy_modularity | x | ||||||||||
head_tail | x | ||||||||||
hierarchical_link_communities | x | ||||||||||
ilouvain | x | x | |||||||||
infomap | x | x | x | ||||||||
infomap_bipartite | x | x | x | x | |||||||
ipca | x | x | x | ||||||||
kclique | x | ||||||||||
kcut | x | ||||||||||
label_propagation | x | ||||||||||
lais2 | x | O(cm + n) | |||||||||
leiden | x | ||||||||||
lemon | x | ||||||||||
lfm | x | x | O(n^2 logn) | ||||||||
louvain | x | ||||||||||
lpam | x | O(2^m) | |||||||||
lpanni | x | O(n) | |||||||||
lswl | x | x | x | ||||||||
lswl_plus | x | x | x | ||||||||
markov_clustering | x | ||||||||||
mcode | x | x | |||||||||
mnmf | x | O(n^2*m+n^2*k) | |||||||||
mod_m | x | O(nd) | |||||||||
mod_r | x | O(nd) | |||||||||
node_perception | x | ||||||||||
multicom | x | ||||||||||
nnsed | x | O(kn^2) | |||||||||
overlapping_seed_set_expansion | x | ||||||||||
paris | x | x | x | ||||||||
percomvc | x | ||||||||||
principled_clustering | x | x | |||||||||
pycombo | x | x | O(n^2 logc) | ||||||||
rb_pots | x | x | x | ||||||||
rber_pots | x | x | |||||||||
ricci_community | x | ||||||||||
r_spectral_clustering | x | ||||||||||
sbm_dl | x | ||||||||||
sbm_dl_nested | x | ||||||||||
scan | x | O(m) | |||||||||
scd | x | ||||||||||
spectral | x | x | |||||||||
significance_communities | x | ||||||||||
sibilarity_antichain | x (DAG) | x | |||||||||
slpa | x | O(kn) | |||||||||
spinglass | x | ||||||||||
surprise_communities | x | x | x | ||||||||
symmnmf | x | ||||||||||
threshold_clustering | x | x | x | ||||||||
tiles | x | x | |||||||||
umstmo | x | ||||||||||
walkscan | x | ||||||||||
walktrap | x | O(n^2 logn) | |||||||||
wCommunity | x | x |