Community Objects

cdlib aims to standardize the representation of network communities. To fulfill such a goal, several Clustering classes are introduced, each capturing specific community characteristics. All classes inherit from the same interface, thus sharing some common functionalities.

In particular, cdlib algorithms can output the following Clustering types:

• NodeClustering: Node communities (either crisp partitions or overlapping groups);

• FuzzyNodeClustering: Overlapping node communities with explicit node-to-community belonging score;

• BiNodeClustering: Clustering of Bipartite graphs (with the explicit representation of class homogeneous communities);

• AttrNodeClustering: Clustering of feature-rich (node-attributed) graphs;

• EdgeClustering: Edge communities;

• TemporalClustering: Clustering of Temporal Networks;

Refer to the following documentation for a complete overview of the methods exposed by cdlib clustering objects.

• Node Clustering
• Fuzzy Node Clustering
• Attributed Node Clustering
• Biparite Node Clustering
• Edge Clustering
• Temporal Clustering

Note

I have a clustering obtained by an algorithm not included in CDlib. Can I load it in a Clustering object to leverage your library’s evaluation and visualization facilities?

Yes, you can.

Just transform your clustering into a list of lists (we represent each community as a list of node IDs) and then create a NodeClustering (or any other Clustering) object.

from cdlib import NodeClustering

communities = [[1,2,3], [4,5,6], [7,8,9,10,11]]
coms = NodeClustering(communities, graph=None, method_name="your_method")

Of course, to compute some evaluation scores/plot community networks, you will also have to pass the original graph (as igraph/networkx object) while building the NodeClustering instance.