The evaluation of Community Discovery algorithms is not an easy task.
CDlib implements two families of evaluation strategies:
- Internal evaluation through quality scores
- External evaluation through partitions comparison
Fitness functions allows to summarize the characteristics of a computed set of communities. CDlib implements the following quality scores:
.. automodule:: cdlib.evaluation
.. autosummary::
:toctree: eval/
avg_distance
avg_embeddedness
average_internal_degree
avg_transitivity
conductance
cut_ratio
edges_inside
expansion
fraction_over_median_degree
hub_dominance
internal_edge_density
normalized_cut
max_odf
avg_odf
flake_odf
scaled_density
significance
size
surprise
triangle_participation_ratio
purity
Among the fitness function a well-defined family of measures is the Modularity-based one:
.. autosummary::
:toctree: eval/
erdos_renyi_modularity
link_modularity
modularity_density
newman_girvan_modularity
z_modularity
Some measures will return an instance of FitnessResult that takes together min/max/mean/std values of the computed index.
.. autosummary::
:toctree: eval/
FitnessResult
It is often useful to compare different graph partition to assess their resemblance (i.e., to perform ground truth testing).
CDlib implements the following partition comparisons scores:
.. autosummary::
:toctree: eval/
adjusted_mutual_information
adjusted_rand_index
f1
nf1
normalized_mutual_information
omega
overlapping_normalized_mutual_information_LFK
overlapping_normalized_mutual_information_MGH
variation_of_information
Some measures will return an instance of MatchingResult that takes together mean and standard deviation values of the computed index.
.. autosummary::
:toctree: eval/
MatchingResult