pylibhipgraph.louvain

pylibhipgraph.louvain#

2025-05-20

2 min read time

Applies to Linux

louvain (ResourceHandle resource_handle, _GPUGraph graph, size_t max_level, float threshold, float resolution, bool_t do_expensive_check)

Compute the modularity optimizing partition of the input graph using the Louvain method.

Parameters#

resource_handleResourceHandle

Handle to the underlying device resources needed for referencing data and running algorithms.

graphSGGraph or MGGraph

The input graph.

max_level: size_t

This controls the maximum number of levels/iterations of the Louvain algorithm. When specified the algorithm will terminate after no more than the specified number of iterations. No error occurs when the algorithm terminates early in this manner.

threshold: float

Modularity gain threshold for each level. If the gain of modularity between 2 levels of the algorithm is less than the given threshold then the algorithm stops and returns the resulting communities.

resolution: float

Called gamma in the modularity formula, this changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities.

do_expensive_checkbool_t

If True, performs more extensive tests on the inputs to ensure validitity, at the expense of increased run time.

Returns#

A tuple containing the hierarchical clustering vertices, clusters and modularity score

Examples#

>>> import pylibhipgraph, cupy, numpy
>>> srcs = cupy.asarray([0, 1, 2], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 2, 0], dtype=numpy.int32)
>>> weights = cupy.asarray([1.0, 1.0, 1.0], dtype=numpy.float32)
>>> resource_handle = pylibhipgraph.ResourceHandle()
>>> graph_props = pylibhipgraph.GraphProperties(
...     is_symmetric=True, is_multigraph=False)
>>> G = pylibhipgraph.SGGraph(
...     resource_handle, graph_props, srcs, dsts, weight_array=weights,
...     store_transposed=True, renumber=False, do_expensive_check=False)
>>> (vertices, clusters, modularity) = pylibhipgraph.louvain(
                            resource_handle, G, 100, 1e-7, 1., False)
>>> vertices
[0, 1, 2]
>>> clusters
[0, 0, 0]
>>> modularity
0.0
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