Metadata-Version: 2.1
Name: graphdot
Version: 0.8a1
Summary: GPU-accelerated graph similarity algorithm library
Home-page: https://gitlab.com/yhtang/graphdot
Author: Yu-Hang Tang
Author-email: Tang.Maxin@gmail.com
License: BSD
Description: # The GraphDot Library
        
        [![pipeline status](https://gitlab.com/yhtang/graphdot/badges/master/pipeline.svg)](https://gitlab.com/yhtang/graphdot/commits/master)
        [![coverage report](https://gitlab.com/yhtang/graphdot/badges/master/coverage.svg)](https://gitlab.com/yhtang/graphdot/commits/master)
        [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
        [![PyPI version](https://badge.fury.io/py/graphdot.svg)](https://badge.fury.io/py/graphdot)
        [![docs](https://readthedocs.org/projects/graphdot/badge/?version=latest&style=flat)](https://graphdot.readthedocs.org/)
        
        GraphDot is a GPU-accelerated Python library that carries out graph dot product operations to compute graph similarity. Currently, the library implements the Marginalized Graph Kernel algorithm, which uses a random walk process to compare subtree patterns and thus defining a generalized graph convolution process. The library can operate on undirected graphs, either weighted or unweighted, that contain arbitrary nodal and edge labels and attributes. It implements state-of-the-art GPU acceleration algorithms and supports versatile customization through just-in-time code generation and compilation.
        
        For more details, please checkout the latest documentation on [readthedocs](https://graphdot.readthedocs.io/).
        
        # Copyright
        
        GraphDot Copyright (c) 2019, The Regents of the University of California,
        through Lawrence Berkeley National Laboratory (subject to receipt of any
        required approvals from the U.S. Dept. of Energy).  All rights reserved.
        
        If you have questions about your rights to use or distribute this software,
        please contact Berkeley Lab's Intellectual Property Office at
        IPO@lbl.gov.
        
        NOTICE.  This Software was developed under funding from the U.S. Department
        of Energy and the U.S. Government consequently retains certain rights.  As
        such, the U.S. Government has been granted for itself and others acting on
        its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
        Software to reproduce, distribute copies to the public, prepare derivative
        works, and perform publicly and display publicly, and to permit other to do
        so.
        
        # Like the package?
        
        Please cite:
        
        - Tang, Yu-Hang, and Wibe A. de Jong. "Prediction of atomization energy using graph kernel and active learning." The Journal of chemical physics 150, no. 4 (2019): 044107.
        - Tang, Yu-Hang, Oguz Selvitopi, Doru Thom Popovici, and Aydın Buluç. "A High-Throughput Solver for Marginalized Graph Kernels on GPU." In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 728-738. IEEE, 2020.
Platform: any
Classifier: Programming Language :: Python
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: docs
