Ensemble Methods

Methods to automate the execution of multiple instances of community detection algorithm(s).

Configuration Objects

Ranges can be specified to automate the execution of a same method while varying (part of) its inputs.

Parameter allows to specify ranges for numeric parameters, while BoolParamter for boolean ones.

Parameter(name, start, end, step)
BoolParameter(name, value)

Multiple Instantiation

Two scenarios often arise when applying community discovery algorithms to a graph:

1. the need to compare the results obtained by a give algorithm while varying its parameters
2. the need to compare the multiple algorithms

cdlib allows to do so by leveraging, respectively, grid_execution and pool.

grid_execution(graph, method, dict], …)	Instantiate the specified community discovery method performing a grid search on the parameter set.
pool(graph, methods, dict], object], …)	Execute on a pool of community discovery internal on the input graph.

Optimal Configuration Search

In some scenarios it could be helpful delegate to the library the selection of the method parameters to obtain a partition that optimize a given quality function. cdlib allows to do so using the methods grid_search and random_search. Finally, pool_grid_filter generalizes such approach allowing to obtain the optimal partitions from a pool of different algorithms.

grid_search(graph, method, dict], object], …)	Returns the optimal partition of the specified graph w.r.t.
random_search(graph, method, dict], object], …)	Returns the optimal partition of the specified graph w.r.t.
pool_grid_filter(graph, methods, dict], …)	Execute a pool of community discovery internal on the input graph.