pylibcugraph.pagerank(ResourceHandle resource_handle, _GPUGraph graph, precomputed_vertex_out_weight_vertices, precomputed_vertex_out_weight_sums, initial_guess_vertices, initial_guess_values, double alpha, double epsilon, size_t max_iterations, bool_t do_expensive_check, fail_on_nonconvergence=True)[source]#

Find the PageRank score for every vertex in a graph by computing an approximation of the Pagerank eigenvector using the power method. The number of iterations depends on the properties of the network itself; it increases when the tolerance descreases and/or alpha increases toward the limiting value of 1.


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

graphSGGraph or MGGraph

The input graph, for either Single or Multi-GPU operations.

precomputed_vertex_out_weight_vertices: device array type

Subset of vertices of graph for precomputed_vertex_out_weight (a performance optimization)

precomputed_vertex_out_weight_sumsdevice array type

Corresponding precomputed sum of outgoing vertices weight (a performance optimization)

initial_guess_verticesdevice array type

Subset of vertices of graph for initial guess for pagerank values (a performance optimization)

initial_guess_valuesdevice array type

Pagerank values for vertices (a performance optimization)


The damping factor alpha represents the probability to follow an outgoing edge, standard value is 0.85. Thus, 1.0-alpha is the probability to “teleport” to a random vertex. Alpha should be greater than 0.0 and strictly lower than 1.0.


Set the tolerance the approximation, this parameter should be a small magnitude value. The lower the tolerance the better the approximation. If this value is 0.0f, cuGraph will use the default value which is 1.0E-5. Setting too small a tolerance can lead to non-convergence due to numerical roundoff. Usually values between 0.01 and 0.00001 are acceptable.


The maximum number of iterations before an answer is returned. This can be used to limit the execution time and do an early exit before the solver reaches the convergence tolerance. If this value is lower or equal to 0 cuGraph will use the default value, which is 100.


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

fail_on_nonconvergencebool (default=True)

If the solver does not reach convergence, raise an exception if fail_on_nonconvergence is True. If fail_on_nonconvergence is False, the return value is a tuple of (pagerank, converged) where pagerank is a cudf.DataFrame as described below, and converged is a boolean indicating if the solver converged (True) or not (False).

The return value varies based on the value of the fail_on_nonconvergence
paramter. If fail_on_nonconvergence is True:

A tuple of device arrays, where the first item in the tuple is a device array containing the vertex identifiers, and the second item is a device array containing the pagerank values for the corresponding vertices. For example, the vertex identifier at the ith element of the vertex array has the pagerank value of the ith element in the pagerank array.

If fail_on_nonconvergence is False:

A three-tuple where the first two items are the device arrays described above, and the third is a bool indicating if the solver converged (True) or not (False).


>>> import pylibcugraph, cupy, numpy
>>> srcs = cupy.asarray([0, 1, 2], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 2, 3], dtype=numpy.int32)
>>> weights = cupy.asarray([1.0, 1.0, 1.0], dtype=numpy.float32)
>>> resource_handle = pylibcugraph.ResourceHandle()
>>> graph_props = pylibcugraph.GraphProperties(
...     is_symmetric=False, is_multigraph=False)
>>> G = pylibcugraph.SGGraph(
...     resource_handle, graph_props, srcs, dsts, weight_array=weights,
...     store_transposed=True, renumber=False, do_expensive_check=False)
>>> (vertices, pageranks) = pylibcugraph.pagerank(
...     resource_handle, G, None, None, None, None, alpha=0.85,
...     epsilon=1.0e-6, max_iterations=500, do_expensive_check=False)
>>> vertices
array([0, 1, 2, 3], dtype=int32)
>>> pageranks
array([0.11615585, 0.21488841, 0.2988108 , 0.3701449 ], dtype=float32)