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:stars: hoshizora: Fast graph analysis engine\nTutorial is [here](https://github.com/hoshizora-project/tutorial).\nYou can try *hoshizora* and [amanogawa](https://github.com/hoshizora-project/amanogawa) on Jupyter on Docker\n\n(**:warning: Currently alpha version. Inner structure and APIs might be changed a lot**)\n\n\n## :sparkles: Features\n* Easy to use\n  * You can use *hoshizora* as a Python library, C++ library and CLI tool\n* Extremely fast\n  * Full native speed\n  * Efficient parallel processing (Can scale to over one hundred cores)\n\nExperimental optimizations are [here](https://github.com/amaya382/hoshizora/tree/experimental).\nNote that experimental branch is unstable.\n\n\n## :soon: Install\nSupporting Linux and macOS\n\n### Python library via pip\n```sh\npip install hoshizora\n```\n\n### From source\nPrerequisites\n* Make\n* CMake 3.0+ \n* Clang++ 3.4+\n* Python 3\n* \\[OPT\\] libnuma\n\n#### CLI\n```sh\ngit submodule init \u0026\u0026 git submodule update\nmake release\n```\n\n#### Python library\n```sh\npython3 setup.py install\n```\n\n\n## :bulb: Example\n### Task: PageRank\n#### Python\n```python\nimport hoshizora as hz\nresult = hz.pagerank(graph_file, num_iters)\n```\n\n#### CLI\n```sh\n./hoshizora-cli ${graph_file} ${num_iters} \u003e result\n```\n\n\n## :persevere: WIP\n* [ ] Querying API\n* [ ] Support dynamic graph\n* [ ] APIs for Graph compaction\n* [ ] Out-of-Core processing\n* [ ] Tests\n* [ ] Many many applications\n\n\n## :green_heart: Acknowledgement\nThis project was supported by IPA (Mito Project)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhoshizora-project%2Fhoshizora","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhoshizora-project%2Fhoshizora","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhoshizora-project%2Fhoshizora/lists"}