Added GPU interface for connected components algorithm

[olegkkruglov]

Description

• CC GPU kernel dispatch — Added select_kernel_dpc.cpp instantiation for connected components (the GPU backend path that dispatches to the GPU kernel), mirroring the same pattern already used by triangle counting.
• CC dispatch chain refactoring — Aligned the connected components dispatch chain with triangle counting: the ops dispatcher (vertex_partitioning_ops.hpp) now extracts topology via csr_topology_builder and passes it directly to backend_default, instead of passing the full graph. Template parameters renamed from Graph to Topology throughout select_kernel.hpp.
• CC CPU kernel topology specialization — Added a topology<int32_t> specialization of vertex_partitioning_kernel_cpu in vertex_partitioning_default_kernel.hpp so the CPU path can accept topology directly (matching the new dispatcher signature).
• DPC++ queue overloads for vertex_partitioning — Added vertex_partitioning(sycl::queue, ...) overloads in vertex_partitioning.hpp and detail/vertex_partitioning_ops.hpp to enable GPU dispatch from user code.
• BUILD file updates — Added frontier primitive dependency, split CC tests into CPU-only (tests) and GPU (gpu_tests) suites, added connected_components to DPC++ examples BUILD.
• csr_topology.hpp — Extended to_device() to also transfer _rows_vertex array (ensures full topology is available on device for CC's afforest algorithm).

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Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

Performance

• I have measured performance for affected algorithms using scikit-learn_bench and provided at least a summary table with measured data, if performance change is expected.
• I have provided justification why performance and/or quality metrics have changed or why changes are not expected.
• I have extended the benchmarking suite and provided a corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.