Features
CM Pipeline Features
The CM Pipeline is a modular pipeline for community detection that contains the following modules:
Graph Cleaning: Removal of parallel and duplicate edges as well as self loops
Community Detection: Clusters an input network with one of Leiden, IKC, and InfoMap.
Cluster Filtration: A pre-processing stage that allows users to filter out clusters that are trees or have size below a given threshold.
Community Statistics Reporting: Generates node and edge count, modularity score, Constant Potts Model score, conductance, and edge-connectivity at multiple stages.
Extensibility: Developers can design new stages and wire in new algorithms. Please see the following document for instructions on how to expand the list of supported clustering algorithms as a developer.
CM++
CM++ Features
CM++ is a module within the CM Pipeline, having the following features:
Function: CM++ refines your existing graph clustering by carving them into well-connected clusters with high minimum cut values.
Flexibility: Users can accompany their definition of a good community with well-connectedness. CM++ works with any clustering algorithm and presently provides build in support for Leiden, IKC, and Infomap.
Dynamic Thresholding: Connectivity thresholds can be constants, or functions of the number of nodes in the cluster, or the minimum node degree of the cluster.
Multi-processing: For better performance, users can specify a larger number of cores to process clusters concurrently.