Python implementation of Hartuv, Erez, and Ron Shamir. "A clustering algorithm based on graph connectivity." Information processing letters 76.4-6 (2000): 175-181.
It provides the sample graph which is used in the publication. It can be created by calling:
G = hcs.create_example_graph()
Another easy way to get your graph is by passing the adjacency matrix to NetworkX
A = np.eye(4) G = nx.convert_matrix.from_numpy_array(A)
The NetworkX graph can be clustered. There are two functions:
HCS(G): should be called if you want to get NetworkX graph that is clustered into the sub-graphs found by HCS.labelled_HCS(G): should be called if you are just interested in the cluster labels. It returns a list with the cluster assignment for each vertex in G.The clustered example graph looks like this:
And the labels are:
array([1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3], dtype=uint16)
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