pylibhipgraph.ecg

pylibhipgraph.ecg#

2025-05-20

3 min read time

Applies to Linux

ecg (ResourceHandle resource_handle, random_state, _GPUGraph graph, double min_weight, size_t ensemble_size, size_t max_level, double threshold, double resolution, bool_t do_expensive_check)

Compute the Ensemble Clustering for Graphs (ECG) partition of the input graph. ECG runs truncated Louvain on an ensemble of permutations of the input graph, then uses the ensemble partitions to determine weights for the input graph. The final result is found by running full Louvain on the input graph using the determined weights.

See https://arxiv.org/abs/1809.05578 for further information.

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

The input graph.

min_weightdouble, optional (default=0.5)

The minimum value to assign as an edgeweight in the ECG algorithm. It should be a value in the range [0,1] usually left as the default value of .05

ensemble_sizesize_t, optional (default=16)

The number of graph permutations to use for the ensemble. The default value is 16, larger values may produce higher quality partitions for some graphs.

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.

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: 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.

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

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) = pylibhipgraph.ecg(resource_handle, G)
# FIXME: Check this docstring example
>>> vertices
[0, 1, 2]
>>> clusters
[0, 0, 0]
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