中心性算法

cuGraph PageRank 是使用我们的图元语库实现的

PageRank

单元测试代码是寻找调用 PageRank 示例的最佳位置。

• SG 实现

• MG 实现

简单 PageRank

此示例假设您已以某种方式创建了 SG 或 MG 图。调用者必须在设备内存中创建 PageRank 向量，并将该向量的原始指针传递给 PageRank 函数。

#include <cugraph/algorithms.hpp>
...
using vertex_t = int32_t;       // or int64_t, whichever is appropriate
using weight_t = float;         // or double, whichever is appropriate
using result_t = weight_t;      // could specify float or double also
raft::handle_t handle;          // Must be configured if MG
auto graph_view = graph.view(); // assumes you have created a graph somehow

result_t constexpr alpha{0.85};
result_t constexpr epsilon{1e-6};

rmm::device_uvector<result_t> pageranks_v(graph_view.number_of_vertices(), handle.get_stream());

// pagerank optionally supports three additional parameters:
//     max_iterations     - maximum number of iterations, if pagerank doesn't coverge by
//                          then we abort
//     has_initial_guess  - if true, values in the pagerank array when the call is initiated
//                          will be used as the initial pagerank values.  These values will
//                          be normalized before use.  If false (the default), the values
//                          in the pagerank array will be set to 1/num_vertices before
//                          starting the computation.
//     do_expensive_check - perform extensive validation of the input data before
//                          executing algorithm.  Off by default.  Note: turning this on
//                          is expensive
cugraph::pagerank(handle, graph_view, nullptr, nullptr, nullptr, vertex_t{0},
                                pageranks_v.data(), alpha, epsilon);

个性化 PageRank

此示例假设您已以某种方式创建了 SG 或 MG 图。调用者必须在设备内存中创建 PageRank 向量，并将该向量的原始指针传递给 PageRank 函数。此外，调用者还必须在设备内存中创建 personalization_vertices 和 personalized_values 向量，填充它们，并传递这些向量的原始指针。

#include <cugraph/algorithms.hpp>
...
using vertex_t = int32_t;                    // or int64_t, whichever is appropriate
using weight_t = float;                      // or double, whichever is appropriate
using result_t = weight_t;                   // could specify float or double also
raft::handle_t handle;                       // Must be configured if MG
auto graph_view = graph.view();              // assumes you have created a graph somehow
vertex_t number_of_personalization_vertices; // Provided by caller

result_t constexpr alpha{0.85};
result_t constexpr epsilon{1e-6};

rmm::device_uvector<result_t> pageranks_v(graph_view.number_of_vertices(), handle.get_stream());
rmm::device_uvector<vertex_t> personalization_vertices(number_of_personalization_vertices, handle.get_stream());
rmm::device_uvector<result_t> personalization_values(number_of_personalization_vertices, handle.get_stream());

//  Populate personalization_vertices, personalization_values with user provided data

// pagerank optionally supports three additional parameters:
//     max_iterations     - maximum number of iterations, if pagerank doesn't coverge by
//                          then we abort
//     has_initial_guess  - if true, values in the pagerank array when the call is initiated
//                          will be used as the initial pagerank values.  These values will
//                          be normalized before use.  If false (the default), the values
//                          in the pagerank array will be set to 1/num_vertices before
//                          starting the computation.
//     do_expensive_check - perform extensive validation of the input data before
//                          executing algorithm.  Off by default.  Note: turning this on
//                          is expensive
cugraph::pagerank(handle, graph_view, nullptr, personalization_vertices.data(),
                                personalization_values.data(), number_of_personalization_vertices,
                                pageranks_v.data(), alpha, epsilon);

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