pylibhipgraph.leiden

pylibhipgraph.leiden#

2025-05-20

2 min read time

Applies to Linux

leiden (ResourceHandle resource_handle, random_state, _GPUGraph graph, size_t max_level, double resolution, double theta, bool_t do_expensive_check)

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

Parameters#

resource_handleResourceHandle

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

random_stateint , optional

Random state to use when generating samples. Optional argument, defaults to a hash of process id, time, and hostname. (See pylibhipgraph.random.HipGraphRandomState)

graphSGGraph or MGGraph

The input graph.

max_level: size_t

This controls the maximum number of levels/iterations of the leiden 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.

resolution: double

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. Defaults to 1.

theta: double

Called theta in the Leiden algorithm, this is used to scale modularity gain in Leiden refinement phase, to compute the probability of joining a random leiden community.

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.Leiden(
                            resource_handle, G, 100, 1., False)
>>> vertices
[0, 1, 2]
>>> clusters
[0, 0, 0]
>>> modularity
0.0
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