diff --git a/cmake/ArrowConfig.cmake b/cmake/ArrowConfig.cmake new file mode 100644 index 000000000..0e35fcf22 --- /dev/null +++ b/cmake/ArrowConfig.cmake @@ -0,0 +1,25 @@ +# Minimal ArrowConfig.cmake for pyarrow bundled libraries +set(ARROW_FOUND TRUE) +set(ARROW_VERSION "23.0.1") + +# Set paths based on pyarrow installation +set(PYARROW_DIR "/home/ubuntu/work/.venv/lib/python3.13/site-packages/pyarrow") +set(ARROW_INCLUDE_DIR "${PYARROW_DIR}/include") +set(ARROW_LIBRARY "${PYARROW_DIR}/libarrow.so.2300") +set(ARROW_LIB_DIR "${PYARROW_DIR}") + +# Create imported target +add_library(Arrow::arrow_shared SHARED IMPORTED) +set_target_properties(Arrow::arrow_shared PROPERTIES + IMPORTED_LOCATION "${ARROW_LIBRARY}" + INTERFACE_INCLUDE_DIRECTORIES "${ARROW_INCLUDE_DIR}" +) + +# Alias for compatibility +add_library(Arrow::arrow ALIAS Arrow::arrow_shared) + +# Provide variables that CMake expects +set(ARROW_LIBRARIES Arrow::arrow_shared) +set(ARROW_INCLUDE_DIRS "${ARROW_INCLUDE_DIR}") + +mark_as_advanced(ARROW_INCLUDE_DIR ARROW_LIBRARY) diff --git a/icebug.egg-info/PKG-INFO b/icebug.egg-info/PKG-INFO new file mode 100644 index 000000000..0a91befd1 --- /dev/null +++ b/icebug.egg-info/PKG-INFO @@ -0,0 +1,335 @@ +Metadata-Version: 2.4 +Name: icebug +Version: 12.1 +Summary: High performance graph analytics backed by read-only memory +Home-page: https://github.com/Ladybug-Memory/icebug +Download-URL: https://pypi.python.org/pypi/icebug +Author: Christian L. Staudt, Henning Meyerhenke, Arun Sharma +Author-email: christian.staudt@kit.edu, meyerhenke@kit.edu, arun@ladybugmem.ai +License: Copyright (c) 2013-2022 Henning Meyerhenke, NetworKit Contributors + Copyright (c) 2013-2017 Karlsruhe Institute of Technology + Copyright (c) 2017-2018 University of Cologne + Copyright (c) 2018-2022 Humboldt-Universität zu Berlin + + MIT License (http://opensource.org/licenses/MIT) + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and + associated documentation files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, publish, distribute, + sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or substantial + portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT + NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES + OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +Project-URL: homepage, https://github.com/Ladybug-Memory/icebug +Project-URL: documentation, https://networkit.github.io/dev-docs/index.html +Project-URL: repository, https://github.com/Ladybug-Memory/icebug +Project-URL: changelog, https://github.com/Ladybug-Memory/icebug/blob/master/CHANGES.md +Keywords: graph algorithm,network analysis,social network,apache arrow,columnar +Platform: any +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: Environment :: Web Environment +Classifier: Environment :: Other Environment +Classifier: Framework :: IPython +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: End Users/Desktop +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: MIT License +Classifier: Natural Language :: English +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: C++ +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Topic :: Scientific/Engineering :: Bio-Informatics +Classifier: Topic :: Scientific/Engineering :: Chemistry +Classifier: Topic :: Scientific/Engineering :: Information Analysis +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Visualization +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +Requires-Dist: scipy +Requires-Dist: numpy +Requires-Dist: pyarrow +Provides-Extra: test +Requires-Dist: pytest; extra == "test" +Requires-Dist: pandas; extra == "test" +Requires-Dist: networkx; extra == "test" +Provides-Extra: vizbridges +Requires-Dist: ipycytoscape; extra == "vizbridges" +Requires-Dist: plotly; extra == "vizbridges" +Requires-Dist: seaborn; extra == "vizbridges" +Requires-Dist: anywidget; extra == "vizbridges" +Requires-Dist: tabulate; extra == "vizbridges" +Provides-Extra: pytest +Requires-Dist: pytest; extra == "pytest" +Requires-Dist: pandas; extra == "pytest" +Requires-Dist: networkx; extra == "pytest" +Requires-Dist: ipycytoscape; extra == "pytest" +Requires-Dist: plotly; extra == "pytest" +Requires-Dist: seaborn; extra == "pytest" +Requires-Dist: anywidget; extra == "pytest" +Requires-Dist: tabulate; extra == "pytest" +Dynamic: author-email +Dynamic: download-url +Dynamic: home-page +Dynamic: platform + +

+ Icebug - Frozen Graph Analytics
+ + + + +

+ +## +[Icebug][icebug] is an open source library for high-performance +graph analysis backed by read-only memory. Due to its heritage, it also supports network/graph analysis on read-write memory, but it can be 5x more CPU and 5x more memory efficient when using columnar memory such as Apache Arrow. + +Its aim is to provide tools for the analysis of large +networks in the size range from thousands to billions of edges. For this +purpose, it implements efficient graph algorithms, many of them parallel to +utilize multicore architectures. These are meant to compute standard measures +of network analysis. Icebug is focused on scalability and comprehensiveness. +Icebug is also a testbed for algorithm engineering and +contains novel algorithms from recently published research (see list of publications below). + +Icebug is a Python module. High-performance algorithms are written in C++ and exposed to Python +via the Cython toolchain. Python in turn gives us the ability to work interactively and a +rich environment of tools for data analysis and scientific computing. +Furthermore, Icebug's core can be built and used as a native library if needed. + +## Requirements + +You will need the following software to install Icebug as a python +package: + +- A modern C++ compiler, e.g.: [g++] (>= 8.1), [clang++] (>= 6.0) or MSVC (>= 14.20) +- OpenMP for parallelism (usually ships with the compiler) +- Python3 (3.9 or higher is supported) + - Development libraries for Python3. The package name depends on your distribution. Examples: + - Debian/Ubuntu: `apt-get install python3-dev` + - RHEL/CentOS: `dnf install python3-devel` + - Windows: Use the official release installer from [www.python.org](https://www.python.org/downloads/windows/) +- [Pip] +- [CMake] version 3.6 or higher (Advised to use system packages if available. Alternative: `pip3 install cmake`) +- Build system: [Make] or [Ninja] +- Cython version 0.29 or higher (e.g., `pip3 install cython`) +- Apache Arrow development libraries (libarrow-dev) + +## Install + +In order to use Icebug, you can either install it via package managers +or build the Python module from source. + +### Install via package manager + +While the most recent version is in general available for all package managers, the number of older downloadable versions differ. + +##### pip + + pip3 install [--user] icebug + +##### conda (channel conda-forge) + + conda config --add channels conda-forge + conda install icebug [-c conda-forge] + +##### brew + + brew install icebug + +##### spack + + spack install py-icebug + +More system-specific information on how to install Icebug on Linux, macOS (both Intel and M1) and Windows-systems can be found [here](https://networkit.github.io/get_started.html). + +### Building the Python module from source + + git clone https://github.com/Ladybug-Memory/icebug icebug + cd icebug + python3 setup.py build_ext [-jX] + pip3 install -e . + +The script will call `cmake` and `ninja` (`make` as fallback) to compile +Icebug as a library, build the extensions and copy it to the top folder. By +default, Icebug will be built with the amount of available cores in +optimized mode. It is possible the add the option `-jN` the number of threads +used for compilation. + +## Usage example + +To get an overview and learn about Icebug's different functions/classes, have a look at our interactive [notebooks-section][notebooks], especially the [Icebug UserGuide]. Note: To view and edit the computed output from the notebooks, it is recommended to use [Jupyter Notebook][jupyter-notebooks]. This requires the prior installation of Icebug. You should really check that out before start working on your network analysis. + +We also provide a Binder-instance of our notebooks. To access this service, you can either click on the badge at the top or follow this [link][binder]. Disclaimer: Due to rebuilds of the underlying image, it can takes some time until your Binder instance is ready for usage. + +If you only want to see in short how Icebug is used - the following example provides a climpse at that. Here we generate a random hyperbolic graph with 100k nodes and compute its communities with the PLM method: + + >>> import icebug as nk + >>> g = nk.generators.HyperbolicGenerator(1e5).generate() + >>> communities = nk.community.detectCommunities(g, inspect=True) + PLM(balanced,pc,turbo) detected communities in 0.14577102661132812 [s] + solution properties: + ------------------- ----------- + # communities 4536 + min community size 1 + max community size 2790 + avg. community size 22.0459 + modularity 0.987243 + ------------------- ----------- + +## Install the C++ Core only + +In case you only want to work with Icebug's C++ core, you can either install it via package +managers or build it from source. + +### Install C++ core via package manager + +##### conda (channel conda-forge) + + conda config --add channels conda-forge + conda install libicebug [-c conda-forge] + +##### brew + + brew install libicebug + +##### spack + + spack install libicebug + +### Building the C++ core from source + +We recommend [CMake] and your preferred build system for building the C++ part of Icebug. + +The following description shows how to use [CMake] in order to build the C++ Core only: + +First you have to create and change to a build directory: (in this case named `build`) + + mkdir build + cd build + +Then call [CMake] to generate files for the `make` build system, specifying the directory of the root `CMakeLists.txt` file (e.g., `..`). After this `make` is called to start the build process: + + cmake .. + make -jX + +To speed up the compilation with make a multi-core machine, you can append `-jX` where X denotes the number of threads to compile with. + +### Use NetworKit as a library + +This paragraph explains how to use the Icebug core C++ library in case it has been built from source. +For how to use it when installed via package managers, best refer to the official documentation ([brew](https://brew.sh), [conda](https://docs.conda.io), [spack](https://spack.readthedocs.io/en/latest)). + +In order to use the previous compiled icebug library, you need to have it installed, and link +it while compiling your project. Use these instructions to compile and install Icebug in `/usr/local`: + + cmake .. + make -jX install + +Once Icebug has been installed, you can use include directives in your +C++\-application as follows: + + #include + +You can compile your source as follows: + + g++ my_file.cpp -licebug + + +### Unit tests + +Building and running Icebug unit tests is not mandatory. +However, as a developer you might want to write and run unit tests for your +code, or if you experience any issues with Icebug, you might want to check +if Icebug runs properly. +The unit tests can only be run from a clone or copy of the repository and not +from a pip installation. In order to run the unit tests, you need to compile +them first. This is done by setting the [CMake] `NETWORKI_BUILD_TESTS` flag to +`ON`: + + cmake -DNETWORKIT_BUILD_TESTS=ON .. + +Unit tests are implemented using GTest macros such as `TEST_F(CentralityGTest, testBetweennessCentrality)`. +Single tests can be executed with: + + ./networkit_tests --gtest_filter=CentralityGTest.testBetweennessCentrality + +Additionally, one can specify the level of the logs outputs by adding `--loglevel `; +supported log levels are: `TRACE`, `DEBUG`, `INFO`, `WARN`, `ERROR`, and `FATAL`. + + +### Compiling with address/leak sanitizers + +Sanitizers are great tools to debug your code. Icebug provides additional [Cmake] flags +to enable address, leak, and undefined behavior sanitizers. +To compile your code with sanitizers, set the [CMake] +`NETWORKIT_WITH_SANITIZERS` to either `address` or `leak`: + + cmake -DNETWORKIT_WITH_SANITIZERS=leak .. + +By setting this flag to `address`, your code will be compiled with the `address` and the `undefined` sanitizers. +Setting it to `leak` also adds the `leak` sanitizer. + + +## Documentation + +The most recent version of the [documentation can be found online](https://networkit.github.io/dev-docs/index.html). + +## Contact + +For questions regarding Icebug, have a look at our [issues-section][issues] and see if there is already an open discussion. If not feel free to open a new issue. +To stay updated about this project, subscribe to our [mailing list][list]. + +## Contributions + +We encourage contributions to the Icebug source code. See the [development guide][devguide] for instructions. For support please contact the [mailing list][list]. + +## Credits + +Icebug is a fork of [NetworKit](https://networkit.github.io/). + +List of contributors can be found on the [NetworKit website credits page](https://networkit.github.io/credits.html). + +## External Code + +The program source includes: +- the *[TLX][tlx]* library +- the *[TTMath]* bignum library + +[mitlicense]: http://opensource.org/licenses/MIT +[ttmath]: http://www.ttmath.org/ +[tlx]: https://github.com/tlx/tlx/ + +## License +The source code of this program is released under the [MIT License][mitlicense]. We ask you to cite us if you use this code in your project (c.f. the publications section below and especially the [technical report](https://arxiv.org/abs/1403.3005)). Feedback is also welcome. + +## Publications +The [Icebug publications page][icepubs] lists the publications on Icebug as a toolkit, on algorithms available +in Icebug, and simply using Icebug. We ask you to cite the appropriate ones if you found Icebug useful for your own research. + +[icepubs]: https://networkit.github.io/publications.html +[list]: https://sympa.cms.hu-berlin.de/sympa/subscribe/icebug +[icebug]: https://github.com/Ladybug-Memory/icebug +[IPython]: https://ipython.readthedocs.io/en/stable/ +[Icebug UserGuide]: https://github.com/Ladybug-Memory/icebug/blob/master/notebooks/User-Guide.ipynb +[notebooks]: https://github.com/Ladybug-Memory/icebug/blob/master/notebooks/ +[g++]: https://gcc.gnu.org +[clang++]: https://clang.llvm.org/ +[Pip]: https://pypi.python.org/pypi/pip +[CMake]: https://cmake.org/ +[Make]: https://www.gnu.org/software/make/ +[Ninja]: https://ninja-build.org/ +[devguide]: https://networkit.github.io/dev-docs/DevGuide.html#devGuide +[issues]: https://github.com/Ladybug-Memory/icebug/issues +[jupyter-notebooks]: https://jupyter.org/install.html +[binder]: https://mybinder.org/v2/gh/networkit/networkit/master?urlpath=lab/tree/notebooks diff --git a/icebug.egg-info/SOURCES.txt b/icebug.egg-info/SOURCES.txt new file mode 100644 index 000000000..5acb6bc01 --- /dev/null +++ b/icebug.egg-info/SOURCES.txt @@ -0,0 +1,1647 @@ +CMakeLists.txt +License.txt +MANIFEST.in +README.md +networkit.pc +pyproject.toml +setup.py +version.py +extlibs/tlx/.clang-format +extlibs/tlx/.clang-tidy +extlibs/tlx/AUTHORS +extlibs/tlx/CMakeLists.txt +extlibs/tlx/CodingStyle.md +extlibs/tlx/Doxyfile +extlibs/tlx/LICENSE +extlibs/tlx/README.md +extlibs/tlx/appveyor.yml +extlibs/tlx/.github/workflows/cmake.yml +extlibs/tlx/.woodpecker/build-test.yaml +extlibs/tlx/.woodpecker/clang-tidy.yaml +extlibs/tlx/misc/mk-doxygen-travis.sh +extlibs/tlx/misc/cmake/tlx-config.cmake.in +extlibs/tlx/misc/cmake/tlx-version.cmake.in +extlibs/tlx/misc/cmake/tlx.pc +extlibs/tlx/misc/format/analyze-source.pl +extlibs/tlx/misc/format/uncrustify.cfg +extlibs/tlx/tests/CMakeLists.txt +extlibs/tlx/tests/algorithm_test.cpp +extlibs/tlx/tests/backtrace_test.cpp +extlibs/tlx/tests/cmdline_parser_example.cpp +extlibs/tlx/tests/cmdline_parser_test.cpp +extlibs/tlx/tests/counting_ptr_test.cpp +extlibs/tlx/tests/delegate_test.cpp +extlibs/tlx/tests/deprecated_test.cpp +extlibs/tlx/tests/die_test.cpp +extlibs/tlx/tests/digest_test.cpp +extlibs/tlx/tests/logger_test.cpp +extlibs/tlx/tests/math_test.cpp +extlibs/tlx/tests/multi_timer_test.cpp +extlibs/tlx/tests/semaphore_test.cpp +extlibs/tlx/tests/siphash_test.cpp +extlibs/tlx/tests/sort_networks_test.cpp +extlibs/tlx/tests/sort_parallel_mergesort_test.cpp +extlibs/tlx/tests/sort_strings_example.cpp +extlibs/tlx/tests/sort_strings_parallel_test.cpp +extlibs/tlx/tests/sort_strings_test.cpp +extlibs/tlx/tests/sort_strings_test.hpp +extlibs/tlx/tests/stack_allocator_test.cpp +extlibs/tlx/tests/string_test.cpp +extlibs/tlx/tests/thread_barrier_test.cpp +extlibs/tlx/tests/thread_pool_test.cpp +extlibs/tlx/tests/algorithm/multiway_merge_benchmark.cpp +extlibs/tlx/tests/algorithm/multiway_merge_benchmark.plot +extlibs/tlx/tests/algorithm/multiway_merge_benchmark_results.pdf +extlibs/tlx/tests/algorithm/multiway_merge_test.cpp +extlibs/tlx/tests/algorithm/random_bipartition_shuffle.cpp +extlibs/tlx/tests/container/btree_speedtest.cpp +extlibs/tlx/tests/container/btree_speedtest.plot +extlibs/tlx/tests/container/btree_speedtest_results.pdf +extlibs/tlx/tests/container/btree_test.cpp +extlibs/tlx/tests/container/d_ary_heap_speedtest.cpp +extlibs/tlx/tests/container/d_ary_heap_speedtest.plot +extlibs/tlx/tests/container/d_ary_heap_speedtest_results.pdf +extlibs/tlx/tests/container/d_ary_heap_test.cpp +extlibs/tlx/tests/container/loser_tree_test.cpp +extlibs/tlx/tests/container/lru_cache_test.cpp +extlibs/tlx/tests/container/radix_heap_test.cpp +extlibs/tlx/tests/container/ring_buffer_test.cpp +extlibs/tlx/tests/container/simple_vector_test.cpp +extlibs/tlx/tests/container/splay_tree_test.cpp +extlibs/tlx/tests/container/string_view_test.cpp +extlibs/tlx/tests/math/aggregate_test.cpp +extlibs/tlx/tests/math/polynomial_regression_test.cpp +extlibs/tlx/tests/meta/call_for_test.cpp +extlibs/tlx/tests/meta/fold_test.cpp +extlibs/tlx/tests/meta/function_chain_test.cpp +extlibs/tlx/tests/meta/function_stack_test.cpp +extlibs/tlx/tests/meta/has_member_test.cpp +extlibs/tlx/tests/meta/log2_test.cpp +extlibs/tlx/tests/meta/vmap_for_test.cpp +extlibs/tlx/tlx/CMakeLists.txt +extlibs/tlx/tlx/algorithm.hpp +extlibs/tlx/tlx/allocator_base.hpp +extlibs/tlx/tlx/backtrace.cpp +extlibs/tlx/tlx/backtrace.hpp +extlibs/tlx/tlx/cmdline_parser.cpp +extlibs/tlx/tlx/cmdline_parser.hpp +extlibs/tlx/tlx/container.hpp +extlibs/tlx/tlx/counting_ptr.hpp +extlibs/tlx/tlx/define.hpp +extlibs/tlx/tlx/delegate.hpp +extlibs/tlx/tlx/die.hpp +extlibs/tlx/tlx/digest.hpp +extlibs/tlx/tlx/logger.hpp +extlibs/tlx/tlx/main.dox +extlibs/tlx/tlx/math.hpp +extlibs/tlx/tlx/meta.hpp +extlibs/tlx/tlx/multi_timer.cpp +extlibs/tlx/tlx/multi_timer.hpp +extlibs/tlx/tlx/port.hpp +extlibs/tlx/tlx/semaphore.hpp +extlibs/tlx/tlx/simple_vector.hpp +extlibs/tlx/tlx/siphash.hpp +extlibs/tlx/tlx/sort.hpp +extlibs/tlx/tlx/stack_allocator.hpp +extlibs/tlx/tlx/string.hpp +extlibs/tlx/tlx/thread_barrier_mutex.hpp +extlibs/tlx/tlx/thread_barrier_spin.hpp +extlibs/tlx/tlx/thread_pool.cpp +extlibs/tlx/tlx/thread_pool.hpp +extlibs/tlx/tlx/timestamp.cpp +extlibs/tlx/tlx/timestamp.hpp +extlibs/tlx/tlx/unused.hpp +extlibs/tlx/tlx/vector_free.hpp +extlibs/tlx/tlx/version.hpp +extlibs/tlx/tlx/algorithm/exclusive_scan.hpp +extlibs/tlx/tlx/algorithm/is_sorted_cmp.hpp +extlibs/tlx/tlx/algorithm/merge_advance.hpp +extlibs/tlx/tlx/algorithm/merge_combine.hpp +extlibs/tlx/tlx/algorithm/multisequence_partition.hpp +extlibs/tlx/tlx/algorithm/multisequence_selection.hpp +extlibs/tlx/tlx/algorithm/multiway_merge.hpp +extlibs/tlx/tlx/algorithm/multiway_merge_splitting.hpp +extlibs/tlx/tlx/algorithm/parallel_multiway_merge.cpp +extlibs/tlx/tlx/algorithm/parallel_multiway_merge.hpp +extlibs/tlx/tlx/algorithm/random_bipartition_shuffle.hpp +extlibs/tlx/tlx/container/btree.hpp +extlibs/tlx/tlx/container/btree_map.hpp +extlibs/tlx/tlx/container/btree_multimap.hpp +extlibs/tlx/tlx/container/btree_multiset.hpp +extlibs/tlx/tlx/container/btree_set.hpp +extlibs/tlx/tlx/container/d_ary_addressable_int_heap.hpp +extlibs/tlx/tlx/container/d_ary_heap.hpp +extlibs/tlx/tlx/container/loser_tree.hpp +extlibs/tlx/tlx/container/lru_cache.hpp +extlibs/tlx/tlx/container/radix_heap.hpp +extlibs/tlx/tlx/container/ring_buffer.hpp +extlibs/tlx/tlx/container/simple_vector.hpp +extlibs/tlx/tlx/container/splay_tree.hpp +extlibs/tlx/tlx/container/string_view.hpp +extlibs/tlx/tlx/define/attribute_always_inline.hpp +extlibs/tlx/tlx/define/attribute_fallthrough.hpp +extlibs/tlx/tlx/define/attribute_format_printf.hpp +extlibs/tlx/tlx/define/attribute_packed.hpp +extlibs/tlx/tlx/define/attribute_warn_unused_result.hpp 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+networkit/test/__pycache__/test_parallel_leiden.cpython-313-pytest-9.0.2.pyc +networkit/test/extension/__init__.py +networkit/test/extension/test.py +networkit/test/gephi/__init__.py \ No newline at end of file diff --git a/icebug.egg-info/dependency_links.txt b/icebug.egg-info/dependency_links.txt new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/icebug.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/icebug.egg-info/not-zip-safe b/icebug.egg-info/not-zip-safe new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/icebug.egg-info/not-zip-safe @@ -0,0 +1 @@ + diff --git a/icebug.egg-info/requires.txt b/icebug.egg-info/requires.txt new file mode 100644 index 000000000..7a8934837 --- /dev/null +++ b/icebug.egg-info/requires.txt @@ -0,0 +1,25 @@ +scipy +numpy +pyarrow + +[pytest] +pytest +pandas +networkx +ipycytoscape +plotly +seaborn +anywidget +tabulate + +[test] +pytest +pandas +networkx + +[vizbridges] +ipycytoscape +plotly +seaborn +anywidget +tabulate diff --git a/icebug.egg-info/top_level.txt b/icebug.egg-info/top_level.txt new file mode 100644 index 000000000..7662e1851 --- /dev/null +++ b/icebug.egg-info/top_level.txt @@ -0,0 +1 @@ +networkit diff --git a/include/networkit/coarsening/CoarsenedGraphView.hpp b/include/networkit/coarsening/CoarsenedGraphView.hpp index 33e4b3bda..8ad0077f3 100644 --- a/include/networkit/coarsening/CoarsenedGraphView.hpp +++ b/include/networkit/coarsening/CoarsenedGraphView.hpp @@ -7,9 +7,6 @@ #ifndef NETWORKIT_COARSENING_COARSENED_GRAPH_VIEW_HPP_ #define NETWORKIT_COARSENING_COARSENED_GRAPH_VIEW_HPP_ -#include -#include -#include #include #include #include @@ -54,8 +51,9 @@ class CoarsenedGraphView { /** * Get the weighted degree of a supernode + * @param countSelfLoopsTwice If true, count self-loops twice (for undirected graphs) */ - edgeweight weightedDegree(node supernode, bool countSelfLoops = false) const; + edgeweight weightedDegree(node supernode, bool countSelfLoopsTwice = false) const; /** * Check if there's an edge between two supernodes @@ -140,19 +138,17 @@ class CoarsenedGraphView { std::vector> supernodeToOriginal; // supernode -> [original_nodes] count numSupernodes; - // Cache for computed neighborhoods to avoid recomputation - mutable std::unordered_map>> neighborCache; - mutable std::mutex cacheMutex; // Mutex to protect neighborCache access - /** - * Compute neighbors of a supernode on-demand + * Get neighbors of a supernode (compute on demand, no caching) */ - std::vector> computeNeighbors(node supernode) const; + std::vector> getNeighbors(node supernode) const { + return computeNeighbors(supernode); + } /** - * Get neighbors of a supernode (cached) + * Compute neighbors of a supernode */ - const std::vector> &getNeighbors(node supernode) const; + std::vector> computeNeighbors(node supernode) const; }; } /* namespace NetworKit */ diff --git a/include/networkit/community/ParallelLeidenView.hpp b/include/networkit/community/ParallelLeidenView.hpp index d8bf28575..9af06d4b4 100644 --- a/include/networkit/community/ParallelLeidenView.hpp +++ b/include/networkit/community/ParallelLeidenView.hpp @@ -11,6 +11,7 @@ #include #include #include +#include #include #include #include @@ -61,12 +62,21 @@ class ParallelLeidenView final : public CommunityDetectionAlgorithm { int VECTOR_OVERSIZE = 10000; private: + struct MoveStats { + count moved = 0; + count movedToSingleton = 0; + count marginalMovesRejected = 0; + double gainMarginSum = 0.0; + double gainMarginMin = std::numeric_limits::max(); + double gainMarginMax = std::numeric_limits::lowest(); + }; + // Template interface to work with both Graph and CoarsenedGraphView template void calculateVolumes(const GraphType &graph); template - void parallelMove(const GraphType &graph); + MoveStats parallelMove(const GraphType &graph); template Partition parallelRefine(const GraphType &graph); @@ -95,7 +105,9 @@ class ParallelLeidenView final : public CommunityDetectionAlgorithm { std::vector communityVolumes; - std::vector> mappings; + // Single composed mapping: accumulated incrementally during inner loop + // Each new mapping is composed into this one, avoiding accumulation + std::vector composedMapping; static constexpr int WORKING_SIZE = 1000; @@ -109,8 +121,18 @@ class ParallelLeidenView final : public CommunityDetectionAlgorithm { bool random; - // Store coarsened graph views to avoid recreating them - std::vector> coarsenedGraphs; + // Current coarsened graph view (only keep current, not all historical) + std::shared_ptr currentCoarsenedView; + + // Reject moves whose gain margin is too small (numerical-noise churn filter). + double moveGainMarginEpsilon = 1e-4; + + // Maximum inner iterations per Leiden iteration. + int maxInnerIterations = 20; + + // Optional convergence stop: minimum relative reduction in community count per inner iter. + // 0.0 disables this criterion. + double minCommunityReduction = 0.0; }; } // namespace NetworKit diff --git a/networkit/cpp/coarsening/CoarsenedGraphView.cpp b/networkit/cpp/coarsening/CoarsenedGraphView.cpp index a96eced66..9db8fc25e 100644 --- a/networkit/cpp/coarsening/CoarsenedGraphView.cpp +++ b/networkit/cpp/coarsening/CoarsenedGraphView.cpp @@ -7,6 +7,7 @@ #include #include #include +#include namespace NetworKit { @@ -51,7 +52,7 @@ count CoarsenedGraphView::degree(node supernode) const { return getNeighbors(supernode).size(); } -edgeweight CoarsenedGraphView::weightedDegree(node supernode, bool countSelfLoops) const { +edgeweight CoarsenedGraphView::weightedDegree(node supernode, bool countSelfLoopsTwice) const { if (!hasNode(supernode)) return 0.0; @@ -60,7 +61,8 @@ edgeweight CoarsenedGraphView::weightedDegree(node supernode, bool countSelfLoop edgeweight totalWeight = 0.0; for (const auto &entry : neighbors) { if (entry.first == supernode) { - if (countSelfLoops) { + totalWeight += entry.second; + if (countSelfLoopsTwice) { totalWeight += entry.second; } } else { @@ -106,7 +108,15 @@ const std::vector &CoarsenedGraphView::getOriginalNodes(node supernode) co std::vector> CoarsenedGraphView::computeNeighbors(node supernode) const { - std::unordered_map aggregatedWeights; + // Hash-map based aggregation dominated runtime on large instances. + // Collect then sort/fold to reduce hashing and allocator pressure. + count incidentEdgeUpperBound = 0; + for (node originalNode : supernodeToOriginal[supernode]) { + incidentEdgeUpperBound += originalGraph.degree(originalNode); + } + + std::vector> rawNeighbors; + rawNeighbors.reserve(incidentEdgeUpperBound); // No locks needed here - supernodeToOriginal and nodeMapping are read-only after construction // Iterate through all original nodes in this supernode @@ -114,51 +124,45 @@ CoarsenedGraphView::computeNeighbors(node supernode) const { // Iterate through neighbors of each original node originalGraph.forNeighborsOf(originalNode, [&](node originalNeighbor, edgeweight weight) { node neighborSupernode = nodeMapping[originalNeighbor]; - // Aggregate weights to the same supernode - aggregatedWeights[neighborSupernode] += weight; + // For internal edges, aggregate each undirected edge only once. + // This mirrors ParallelPartitionCoarsening semantics. + if (neighborSupernode == supernode && originalNode < originalNeighbor) { + return; + } + rawNeighbors.emplace_back(neighborSupernode, weight); }); } - // Convert to vector format - std::vector> neighbors; - neighbors.reserve(aggregatedWeights.size()); - - for (const auto &entry : aggregatedWeights) { - if (entry.second > 0.0) { // Only include edges with positive weight - neighbors.emplace_back(entry.first, entry.second); - } + if (rawNeighbors.empty()) { + return {}; } - return neighbors; -} + std::sort(rawNeighbors.begin(), rawNeighbors.end(), + [](const std::pair &a, const std::pair &b) { + return a.first < b.first; + }); -const std::vector> & -CoarsenedGraphView::getNeighbors(node supernode) const { - // Double-checked locking pattern to minimize lock contention - // First check without lock (fast path for cached entries) - { - std::lock_guard lock(cacheMutex); - auto it = neighborCache.find(supernode); - if (it != neighborCache.end()) { - return it->second; + // Fold adjacent entries with same neighbor supernode. + std::vector> neighbors; + neighbors.reserve(rawNeighbors.size()); + node currentNeighbor = rawNeighbors[0].first; + edgeweight currentWeight = rawNeighbors[0].second; + for (size_t i = 1; i < rawNeighbors.size(); ++i) { + if (rawNeighbors[i].first == currentNeighbor) { + currentWeight += rawNeighbors[i].second; + } else { + if (currentWeight > 0.0) { + neighbors.emplace_back(currentNeighbor, currentWeight); + } + currentNeighbor = rawNeighbors[i].first; + currentWeight = rawNeighbors[i].second; } } - - // Compute neighbors WITHOUT holding the lock (slow path) - // This is safe because computeNeighbors() only reads immutable data - auto result = computeNeighbors(supernode); - - // Now acquire lock and insert the result - std::lock_guard lock(cacheMutex); - // Check again in case another thread computed it while we were computing - auto it = neighborCache.find(supernode); - if (it != neighborCache.end()) { - // Another thread beat us to it, use their result - return it->second; + if (currentWeight > 0.0) { + neighbors.emplace_back(currentNeighbor, currentWeight); } - // We're the first to compute it, insert our result - auto [insertIt, inserted] = neighborCache.emplace(supernode, std::move(result)); - return insertIt->second; + + return neighbors; } } /* namespace NetworKit */ diff --git a/networkit/cpp/coarsening/test/CoarseningGTest.cpp b/networkit/cpp/coarsening/test/CoarseningGTest.cpp index 3a6ebeb26..a66e4f550 100644 --- a/networkit/cpp/coarsening/test/CoarseningGTest.cpp +++ b/networkit/cpp/coarsening/test/CoarseningGTest.cpp @@ -9,6 +9,7 @@ #include #include +#include #include #include #include @@ -336,4 +337,40 @@ TEST_F(CoarseningGTest, testClusteringProjectorWithNClusterings) { } } } + +TEST_F(CoarseningGTest, testCoarsenedGraphViewMatchesParallelPartitionCoarseningWeights) { + GraphW G(5, true, false); + G.addEdge(0, 1, 1.0); + G.addEdge(1, 2, 2.0); + G.addEdge(0, 2, 3.0); + G.addEdge(3, 4, 4.0); + G.addEdge(2, 3, 5.0); + G.addEdge(0, 0, 6.0); + + Partition zeta(G.numberOfNodes()); + zeta[0] = 0; + zeta[1] = 0; + zeta[2] = 0; + zeta[3] = 1; + zeta[4] = 1; + zeta.setUpperBound(2); + + ParallelPartitionCoarsening coarsening(G, zeta, false); + coarsening.run(); + GraphW coarseGraph = coarsening.getCoarseGraph(); + + CoarsenedGraphView view(G, zeta); + + ASSERT_EQ(coarseGraph.numberOfNodes(), view.numberOfNodes()); + ASSERT_EQ(coarseGraph.numberOfEdges(), view.numberOfEdges()); + + for (node u = 0; u < view.numberOfNodes(); ++u) { + EXPECT_DOUBLE_EQ(coarseGraph.weightedDegree(u, false), view.weightedDegree(u, false)); + EXPECT_DOUBLE_EQ(coarseGraph.weightedDegree(u, true), view.weightedDegree(u, true)); + + for (node v = 0; v < view.numberOfNodes(); ++v) { + EXPECT_DOUBLE_EQ(coarseGraph.weight(u, v), view.weight(u, v)); + } + } +} } /* namespace NetworKit */ diff --git a/networkit/cpp/community/ParallelLeidenView.cpp b/networkit/cpp/community/ParallelLeidenView.cpp index 82bb90e3e..ad7759bc3 100644 --- a/networkit/cpp/community/ParallelLeidenView.cpp +++ b/networkit/cpp/community/ParallelLeidenView.cpp @@ -5,6 +5,7 @@ */ #include +#include namespace NetworKit { @@ -14,12 +15,53 @@ ParallelLeidenView::ParallelLeidenView(const Graph &graph, int iterations, bool random(randomize) { this->result = Partition(graph.numberOfNodes()); this->result.allToSingletons(); + + if (const char *epsilonEnv = std::getenv("NETWORKIT_LEIDEN_MOVE_EPS")) { + try { + const double parsed = std::stod(epsilonEnv); + if (parsed >= 0.0) { + moveGainMarginEpsilon = parsed; + } + } catch (...) { + // Keep default if parsing fails. + } + } + if (const char *maxInnerEnv = std::getenv("NETWORKIT_LEIDEN_MAX_INNER")) { + try { + const int parsed = std::stoi(maxInnerEnv); + if (parsed > 0) { + maxInnerIterations = parsed; + } + } catch (...) { + // Keep default if parsing fails. + } + } + if (const char *vectorOversizeEnv = std::getenv("NETWORKIT_LEIDEN_VECTOR_OVERSIZE")) { + try { + const int parsed = std::stoi(vectorOversizeEnv); + if (parsed >= 1) { + VECTOR_OVERSIZE = parsed; + } + } catch (...) { + // Keep default if parsing fails. + } + } + if (const char *minReductionEnv = std::getenv("NETWORKIT_LEIDEN_MIN_COMM_REDUCTION")) { + try { + const double parsed = std::stod(minReductionEnv); + if (parsed >= 0.0) { + minCommunityReduction = parsed; + } + } catch (...) { + // Keep default if parsing fails. + } + } } ParallelLeidenView::~ParallelLeidenView() { - // Explicitly clear resources in proper order to avoid double-free - coarsenedGraphs.clear(); - mappings.clear(); + currentCoarsenedView.reset(); + composedMapping.clear(); + composedMapping.shrink_to_fit(); communityVolumes.clear(); } @@ -34,36 +76,63 @@ void ParallelLeidenView::run() { INFO(numberOfIterations, " Leiden iteration(s) left"); numberOfIterations--; changed = false; + INFO("Using move gain epsilon=", std::setprecision(6), moveGainMarginEpsilon); + INFO("Using max inner iterations=", maxInnerIterations, + " | vector oversize=", VECTOR_OVERSIZE, + " | min community reduction=", minCommunityReduction); + + // Initialize composed mapping to identity + composedMapping.clear(); + composedMapping.resize(G->numberOfNodes()); + G->parallelForNodes([&](node u) { composedMapping[u] = u; }); // Start with the original graph const Graph *currentGraph = G; - std::shared_ptr currentCoarsenedView = nullptr; + currentCoarsenedView.reset(); Partition refined; - // Calculate volumes for the current graph (either original or coarsened view) - if (currentCoarsenedView) { - calculateVolumes(*currentCoarsenedView); - } else { - calculateVolumes(*currentGraph); - } + // Calculate volumes for the current graph + calculateVolumes(*currentGraph); int innerIterations = 0; - const int maxInnerIterations = 100; // Safety limit to prevent infinite loops + count previousCommunities = result.numberOfSubsets(); + INFO("Starting inner loop with ", result.numberOfSubsets(), " communities"); do { innerIterations++; if (innerIterations > maxInnerIterations) { + INFO("Reached max inner iterations (", maxInnerIterations, ")"); break; } handler.assureRunning(); // Parallel move phase + MoveStats moveStats; if (currentCoarsenedView) { - parallelMove(*currentCoarsenedView); + moveStats = parallelMove(*currentCoarsenedView); } else { - parallelMove(*currentGraph); + moveStats = parallelMove(*currentGraph); } + const double avgGainMargin = + moveStats.moved ? (moveStats.gainMarginSum / moveStats.moved) : 0.0; + const double minGainMargin = + moveStats.moved ? moveStats.gainMarginMin : 0.0; + const double maxGainMargin = + moveStats.moved ? moveStats.gainMarginMax : 0.0; + const count candidateMoves = moveStats.moved + moveStats.marginalMovesRejected; + const double rejectedPct = candidateMoves + ? (100.0 * moveStats.marginalMovesRejected + / static_cast(candidateMoves)) + : 0.0; + INFO("Inner iter ", innerIterations, ": moved ", moveStats.moved, " nodes, ", + result.numberOfSubsets(), " communities", + " | singleton moves=", moveStats.movedToSingleton, + " | marginal rejected=", moveStats.marginalMovesRejected, + " (", std::setprecision(4), rejectedPct, "%)", + " | avg gain margin=", std::setprecision(6), avgGainMargin, + " | min/max gain margin=", minGainMargin, "/", maxGainMargin); + // If each community consists of exactly one node we're done count numNodes = currentCoarsenedView ? currentCoarsenedView->numberOfNodes() : currentGraph->numberOfNodes(); @@ -82,14 +151,11 @@ void ParallelLeidenView::run() { handler.assureRunning(); - // Create coarsened view instead of new graph - // For coarsened views, we need to map the refined partition back to the original graph + // Create coarsened view if (currentCoarsenedView) { - // Create a refined partition in terms of the original graph nodes Partition originalRefined(G->numberOfNodes()); originalRefined.setUpperBound(refined.upperBound()); - // Map each coarse node's refined assignment back to original nodes for (node coarseNode = 0; coarseNode < currentCoarsenedView->numberOfNodes(); ++coarseNode) { const auto &originalNodes = currentCoarsenedView->getOriginalNodes(coarseNode); @@ -105,48 +171,55 @@ void ParallelLeidenView::run() { auto map = ppcView.getFineToCoarseNodeMapping(); const auto &oldNodeMapping = currentCoarsenedView->getNodeMapping(); - // Create new partition for the new coarsened view Partition p(newCoarsenedView->numberOfNodes()); p.setUpperBound(result.upperBound()); - // Map communities through the chain of coarsenings BEFORE moving map - // Capture map by value and other refs explicitly G->parallelForNodes([map = map, &p, this, &oldNodeMapping](node originalNode) { node newCoarseNode = map[originalNode]; - // Find what community this original node belonged to node oldCoarseNode = oldNodeMapping[originalNode]; p[newCoarseNode] = result[oldCoarseNode]; }); - // Update mappings after using map - mappings.emplace_back(std::move(map)); + // Since each coarsened view is rebuilt from the original graph, this map already + // represents original -> current-coarse. + composedMapping = std::move(map); result = std::move(p); - coarsenedGraphs.push_back(newCoarsenedView); + result.compact(true); currentCoarsenedView = newCoarsenedView; } else { - // First coarsening from original graph ParallelPartitionCoarseningView ppcView(*currentGraph, refined); ppcView.run(); auto newCoarsenedView = ppcView.getCoarsenedGraphView(); auto map = ppcView.getFineToCoarseNodeMapping(); - // Maintain Partition, add every coarse Node to the community its fine Nodes were in Partition p(newCoarsenedView->numberOfNodes()); p.setUpperBound(result.upperBound()); - // Capture map by value and other refs explicitly currentGraph->parallelForNodes( [map = map, &p, this](node u) { p[map[u]] = result[u]; }); - mappings.emplace_back(std::move(map)); + composedMapping = std::move(map); result = std::move(p); - - // Store the coarsened view and use it for the next iteration - coarsenedGraphs.push_back(newCoarsenedView); + result.compact(true); currentCoarsenedView = newCoarsenedView; - currentGraph = nullptr; // No longer using the original graph directly + currentGraph = nullptr; } + + calculateVolumes(*currentCoarsenedView); + + const count currentCommunities = result.numberOfSubsets(); + if (minCommunityReduction > 0.0 && previousCommunities > 0) { + const double reduction = + static_cast(previousCommunities - currentCommunities) + / static_cast(previousCommunities); + if (reduction < minCommunityReduction) { + INFO("Stopping inner loop: community reduction ", reduction, + " below threshold ", minCommunityReduction); + break; + } + } + previousCommunities = currentCommunities; } while (true); @@ -163,10 +236,9 @@ void ParallelLeidenView::calculateVolumes(const GraphType &graph) { auto timer = Aux::Timer(); timer.start(); - // thread safe reduction. Avoid atomic calculation of total graph volume for unweighted graphs. communityVolumes.clear(); communityVolumes.resize(result.upperBound() + VECTOR_OVERSIZE); - inverseGraphVolume = 0.0; // Reset to 0 before accumulation + inverseGraphVolume = 0.0; if (graph.isWeighted()) { std::vector threadVolumes(omp_get_max_threads()); @@ -193,47 +265,42 @@ void ParallelLeidenView::calculateVolumes(const GraphType &graph) { void ParallelLeidenView::flattenPartition() { auto timer = Aux::Timer(); timer.start(); - if (mappings.empty()) { + if (composedMapping.empty()) { return; } - // Create a new partition with size |V(G)| (the fine/bigger Graph) + // composedMapping already contains the composed mapping: original -> final coarse node Partition flattenedPartition(G->numberOfNodes()); flattenedPartition.setUpperBound(result.upperBound()); - int i = mappings.size() - 1; - std::vector &lower = mappings[i--]; - while (i >= 0) { - // iteratively "resolve" (i.e compose) mappings. Let "lower" be a mapping thats - // below "higher" in the hierarchy (i.e. of a later aggregation) - // If higher[index] = z and lower[z] = x then set higher[index] = x - std::vector &upper = mappings[i--]; - for (auto &idx : upper) { - idx = lower[idx]; - } - lower = upper; - } - G->parallelForNodes([&](node a) { flattenedPartition[a] = lower[a]; }); + G->parallelForNodes([&](node a) { + flattenedPartition[a] = result[composedMapping[a]]; + }); flattenedPartition.compact(true); result = flattenedPartition; - mappings.clear(); - coarsenedGraphs.clear(); // Clear the stored views + composedMapping.clear(); + composedMapping.shrink_to_fit(); + currentCoarsenedView.reset(); TRACE("Flattening partition took " + timer.elapsedTag()); } template -void ParallelLeidenView::parallelMove(const GraphType &graph) { +ParallelLeidenView::MoveStats ParallelLeidenView::parallelMove(const GraphType &graph) { DEBUG("Local Moving : ", graph.numberOfNodes(), " Nodes "); std::vector moved(omp_get_max_threads(), 0); + std::vector movedToSingleton(omp_get_max_threads(), 0); + std::vector marginalMovesRejected(omp_get_max_threads(), 0); + std::vector gainMarginSum(omp_get_max_threads(), 0.0); + std::vector gainMarginMin(omp_get_max_threads(), std::numeric_limits::max()); + std::vector gainMarginMax(omp_get_max_threads(), std::numeric_limits::lowest()); std::vector totalNodesPerThread(omp_get_max_threads(), 0); std::atomic_int singleton(0); - // Only insert nodes to the queue when they're not already in it. std::vector inQueue(graph.upperNodeIdBound()); for (auto &val : inQueue) { val.store(false); } std::queue> queue; - std::mutex qlock; // queue lock - std::condition_variable workAvailable; // waiting/notifying for new Nodes + std::mutex qlock; + std::condition_variable workAvailable; std::atomic_bool resize(false); std::atomic_int waitingForResize(0); @@ -263,7 +330,6 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { currentNodes.reserve(tshare); std::vector newNodes; newNodes.reserve(WORKING_SIZE); - // cutWeight[Community] returns cut of Node to Community std::vector cutWeights(communityVolumes.capacity()); std::vector pointers; int start = tshare * order[omp_get_thread_num()]; @@ -281,7 +347,6 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { do { handler.assureRunning(); for (node u : currentNodes) { - // If a vector resize is needed, yield until done if (resize) { waitingForResize++; while (resize) { @@ -296,7 +361,6 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { index bestCommunity = none; double degree = 0; for (auto z : pointers) { - // Reset the clearlist : Set all cutweights to 0 and clear the pointer vector cutWeights[z] = 0; } pointers.clear(); @@ -311,15 +375,13 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { } else { cutWeights[neighborCommunity] += ew; } - degree += ew; // keep track of the nodes degree. Loops count twice + degree += ew; }); if (pointers.empty()) continue; - // Determine Modularity delta for all neighbor communities for (auto community : pointers) { - // "Moving" a node to its current community is pointless if (community != currentCommunity) { double delta; delta = modularityDelta(cutWeights[community], degree, @@ -333,13 +395,29 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { double modThreshold = modularityThreshold( cutWeights[currentCommunity], communityVolumes[currentCommunity], degree); - if (0 > modThreshold || maxDelta > modThreshold) { - moved[omp_get_thread_num()]++; - if (0 > maxDelta) { // move node to empty community + bool acceptedMove = false; + bool singletonMove = (0 > maxDelta); + double gainMargin = 0.0; + if (singletonMove) { + gainMargin = -modThreshold; + acceptedMove = (0 > modThreshold); + } else { + gainMargin = maxDelta - modThreshold; + acceptedMove = (maxDelta > modThreshold); + } + if (acceptedMove && gainMargin <= moveGainMarginEpsilon) { + marginalMovesRejected[omp_get_thread_num()]++; + acceptedMove = false; + } + + if (acceptedMove) { + const int tid = omp_get_thread_num(); + moved[tid]++; + if (singletonMove) { singleton++; + movedToSingleton[tid]++; bestCommunity = upperBound++; if (bestCommunity >= communityVolumes.size()) { - // Wait until all other threads yielded, then increase vector size bool expected = false; if (resize.compare_exchange_strong(expected, true)) { vectorSize += VECTOR_OVERSIZE; @@ -347,7 +425,6 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { while (waitingForResize < tcount - 1) { std::this_thread::yield(); } - // all other threads are yielding, so resize is fine communityVolumes.resize(vectorSize); expected = true; resize.compare_exchange_strong(expected, false); @@ -361,6 +438,9 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { } } } + gainMarginSum[tid] += gainMargin; + gainMarginMin[tid] = std::min(gainMarginMin[tid], gainMargin); + gainMarginMax[tid] = std::max(gainMarginMax[tid], gainMargin); result[u] = bestCommunity; #pragma omp atomic communityVolumes[bestCommunity] += degree; @@ -371,18 +451,14 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { inQueue[u].compare_exchange_strong(expected, false); assert(expected); graph.forNeighborsOf(u, [&](node neighbor, edgeweight) { - // Only add the node to the queue if it's not already - // in it, and it's not the Node we're currently moving if (result[neighbor] != bestCommunity && neighbor != u) { expected = false; if (inQueue[neighbor].compare_exchange_strong(expected, true)) { newNodes.push_back(neighbor); - // push new nodes to the queue in WORKING_SIZE steps if (newNodes.size() == WORKING_SIZE) { qlock.lock(); queue.emplace(std::move(newNodes)); qlock.unlock(); - // Notify threads that new is available workAvailable.notify_all(); newNodes.clear(); newNodes.reserve(WORKING_SIZE); @@ -394,7 +470,6 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { } } - // queue check/wait totalNodesPerThread[omp_get_thread_num()] += currentNodes.size(); if (!newNodes.empty()) { std::swap(currentNodes, newNodes); @@ -406,15 +481,14 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { if (!queue.empty()) { std::swap(currentNodes, queue.front()); queue.pop(); - } else { // queue empty && newNodes empty + } else { waitingForNodes++; - if (waitingForNodes < tcount) { // Not all nodes are done yet, wait for new work + if (waitingForNodes < tcount) { waitingForResize++; while (queue.empty() && waitingForNodes < tcount) { workAvailable.wait(uniqueLock); } if (waitingForNodes < tcount) { - // Notified and not all done means there's new work std::swap(currentNodes, queue.front()); queue.pop(); waitingForNodes--; @@ -422,7 +496,7 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { continue; } } - uniqueLock.unlock(); // Notified and all done, stop. + uniqueLock.unlock(); workAvailable.notify_all(); break; } @@ -434,15 +508,35 @@ void ParallelLeidenView::parallelMove(const GraphType &graph) { result.setUpperBound(upperBound); assert(queue.empty()); assert(waitingForNodes == tcount); + count totalMoved = std::accumulate(moved.begin(), moved.end(), (count)0); if (Aux::Log::isLogLevelEnabled(Aux::Log::LogLevel::DEBUG)) { - count totalMoved = std::accumulate(moved.begin(), moved.end(), (count)0); count totalWorked = std::accumulate(totalNodesPerThread.begin(), totalNodesPerThread.end(), (count)0); - tlx::unused(totalMoved); tlx::unused(totalWorked); DEBUG("Total worked: ", totalWorked, " Total moved: ", totalMoved, " moved to singleton community: ", singleton); } + MoveStats stats; + stats.moved = totalMoved; + stats.movedToSingleton = + std::accumulate(movedToSingleton.begin(), movedToSingleton.end(), static_cast(0)); + stats.marginalMovesRejected = std::accumulate(marginalMovesRejected.begin(), + marginalMovesRejected.end(), + static_cast(0)); + stats.gainMarginSum = std::accumulate(gainMarginSum.begin(), gainMarginSum.end(), 0.0); + if (totalMoved > 0) { + for (int i = 0; i < omp_get_max_threads(); ++i) { + if (moved[i] == 0) { + continue; + } + stats.gainMarginMin = std::min(stats.gainMarginMin, gainMarginMin[i]); + stats.gainMarginMax = std::max(stats.gainMarginMax, gainMarginMax[i]); + } + } else { + stats.gainMarginMin = 0.0; + stats.gainMarginMax = 0.0; + } + return stats; } template @@ -452,15 +546,14 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { DEBUG("Starting refinement with ", result.numberOfSubsets(), " partitions"); std::vector singleton(refined.upperBound(), true); std::vector cutCtoSminusC(refined.upperBound()); - std::vector refinedVolumes(refined.upperBound()); // Community Volumes P_refined + std::vector refinedVolumes(refined.upperBound()); std::vector locks(refined.upperBound()); std::vector nodes(graph.upperNodeIdBound(), none); #pragma omp parallel { std::vector neighComms; - // Keeps track of relevant Neighbor communities. Needed to reset the clearlist fast - std::vector cutWeights(refined.upperBound()); // cut from Node to Communities + std::vector cutWeights(refined.upperBound()); auto &mt = Aux::Random::getURNG(); #pragma omp for for (omp_index u = 0; u < static_cast(graph.upperNodeIdBound()); u++) { @@ -469,8 +562,6 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { graph.forNeighborsOf(u, [&](node neighbor, edgeweight ew) { if (u != neighbor) { if (result[neighbor] == result[u]) { - // Cut to communities in the refined partition that - // are in the same community in the original partition cutCtoSminusC[u] += ew; } } else { @@ -494,11 +585,11 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { #pragma omp for schedule(dynamic, WORKING_SIZE) for (omp_index i = 0; i < static_cast(nodes.size()); i++) { node u = nodes[i]; - if (u == none || !singleton[u]) { // only consider singletons + if (u == none || !singleton[u]) { continue; } - index S = result[u]; // Node's community ID in the original partition (S) - for (auto neighComm : neighComms) { // Reset the clearlist : Set all cutweights to 0 + index S = result[u]; + for (auto neighComm : neighComms) { if (neighComm != none) cutWeights[neighComm] = 0; } @@ -506,11 +597,9 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { neighComms.clear(); std::vector criticalNodes; - // Nodes whose community ID equals their Node ID. These may be singletons that can - // affect the cut which we need to update later double degree = 0; - graph.forNeighborsOf(u, [&](node neighbor, edgeweight ew) { // Calculate degree and cut + graph.forNeighborsOf(u, [&](node neighbor, edgeweight ew) { degree += ew; if (neighbor != u) { if (S == result[neighbor]) { @@ -518,10 +607,8 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { if (z == neighbor) { criticalNodes.push_back(neighbor); } - // We don't need to remember the weight of that edge since it's - // already saved in cutWeights if (cutWeights[z] == 0) - neighComms.push_back(z); // Keep track of neighbor communities + neighComms.push_back(z); cutWeights[z] += ew; } } else { @@ -529,11 +616,11 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { } }); if (cutCtoSminusC[u] < this->gamma * degree * (communityVolumes[S] - degree) - * inverseGraphVolume) { // R-Set Condition + * inverseGraphVolume) { continue; } - if (cutWeights[u] != 0) { // Node has been moved -> not a singleton anymore. Stop. + if (cutWeights[u] != 0) { continue; } @@ -541,7 +628,6 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { index bestC = none; double bestDelta = std::numeric_limits::lowest(); int idx; - // Determine Community that yields highest modularity delta auto bestCommunity = [&] { for (unsigned int j = 0; j < neighComms.size(); j++) { index C = neighComms[j]; @@ -550,36 +636,29 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { } delta = modularityDelta(cutWeights[C], degree, refinedVolumes[C]); - if (delta < 0) { // modThreshold is 0, since cutw(v,C-) = 0 and volw(C-) = 0 + if (delta < 0) { continue; } auto volC = refinedVolumes[C]; if (delta > bestDelta && cutCtoSminusC[C] >= this->gamma * volC * (communityVolumes[S] - volC) - * inverseGraphVolume) { // T-Set Condition + * inverseGraphVolume) { bestDelta = delta; bestC = C; idx = j; } } }; - // To update cut values in case neighbors of this node have moved in the meantime auto updateCut = [&] { for (node &neighbor : criticalNodes) { if (neighbor != none) { index neighborCommunity = refined[neighbor]; if (neighborCommunity != neighbor) { if (cutWeights[neighborCommunity] == 0) { - // remember to clear the vector, this community was not saved - // initially since the neighbor moved to it later neighComms.push_back(neighborCommunity); } - // cutWeights[Neighbor] is the weight of the edge between Node and - // Neighbor, since Neighbor was a singleton cutWeights[neighborCommunity] += cutWeights[neighbor]; - // Clear cutWeights entry beforehand, so we can "erase" bestC - // from the pointers vector by replacing it with "none" cutWeights[neighbor] = 0; neighbor = none; } @@ -590,14 +669,10 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { if (bestC == none) { continue; } - lockLowerFirst(u, bestC, locks); // avoid deadlocks - // If this node is no longer a singleton, stop. + lockLowerFirst(u, bestC, locks); if (singleton[u]) { - // Target community still contains its "host" node? If not,this community is - // now empty, choose a new one. while (bestC != none && refined[bestC] != bestC) { locks[bestC].unlock(); - // This makes sure it won't be considered in the next bestCommunity() call neighComms[idx] = none; bestC = none; bestDelta = std::numeric_limits::lowest(); @@ -608,7 +683,6 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { if (u < bestC) { locks[bestC].lock(); } else { - // temporarily release lock on current Node to avoid deadlocks locks[u].unlock(); lockLowerFirst(u, bestC, locks); } @@ -621,7 +695,7 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { } } if (bestC == none) { - locks[u].unlock(); // bestC was already unlocked in the loop above + locks[u].unlock(); continue; } singleton[bestC] = false; @@ -639,12 +713,12 @@ Partition ParallelLeidenView::parallelRefine(const GraphType &graph) { return refined; } -// Explicit template instantiations template void ParallelLeidenView::calculateVolumes(const Graph &graph); template void ParallelLeidenView::calculateVolumes(const CoarsenedGraphView &graph); -template void ParallelLeidenView::parallelMove(const Graph &graph); -template void ParallelLeidenView::parallelMove(const CoarsenedGraphView &graph); +template ParallelLeidenView::MoveStats ParallelLeidenView::parallelMove(const Graph &graph); +template ParallelLeidenView::MoveStats +ParallelLeidenView::parallelMove(const CoarsenedGraphView &graph); template Partition ParallelLeidenView::parallelRefine(const Graph &graph); template Partition ParallelLeidenView::parallelRefine(const CoarsenedGraphView &graph); diff --git a/networkit/test/test_leiden_validation.py b/networkit/test/test_leiden_validation.py index 89cf9856a..bc2f47331 100644 --- a/networkit/test/test_leiden_validation.py +++ b/networkit/test/test_leiden_validation.py @@ -128,9 +128,9 @@ def test_triangle(): def test_connected_cycle(): - """Test with a 20-node cycle - should find 1 community.""" + """Test with a 20-node cycle - should find 1 community at low gamma.""" print("\n=== Test 3: 20-Node Cycle ===") - print("Ground truth: 1 connected cycle = 1 community") + print("Ground truth: with low gamma and deterministic execution, cycle => 1 community") n = 20 edges = [(i, (i + 1) % n) for i in range(n)] @@ -141,9 +141,16 @@ def test_connected_cycle(): print(f"Graph: {graph.numberOfNodes()} nodes, {graph.numberOfEdges()} edges") - leiden = nk.community.ParallelLeidenView(graph, iterations=10, gamma=1.0, randomize=False) - leiden.run() - partition = leiden.getPartition() + old_threads = nk.getCurrentNumberOfThreads() + nk.setNumberOfThreads(1) + try: + # gamma=1.0 often favors splitting ring-like graphs into multiple groups. + # Use low gamma for this test to validate deterministic merge behavior. + leiden = nk.community.ParallelLeidenView(graph, iterations=10, gamma=0.1, randomize=False) + leiden.run() + partition = leiden.getPartition() + finally: + nk.setNumberOfThreads(old_threads) n_communities = 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