|PyPI Version| |Python Versions| |Test| |Coverage| |License| |Documentation|
Python First-Order Graph Property Library
==================================================
.. image:: docs/img/pypg_color.png
:align: center
:height: 150
:alt: pygplib
``Pygplib`` (Python First-Order Graph Property Library) is a Python module
for constructing, manipulating, and encoding graph properties expressible
with first-order logic of graphs.
It serves as a prototyping tool to tackle with
various graph related applications.
It provides access to state-of-the-art satisfiability technologies
without advanced knowledge.
Basic steps to follow are :
- Express a graph property of interest as a first-order formula.
- Set a graph structure, and encode a first-order formula into CNF,
a canonical normal form for propositional formulas.
- Apply satisfiability tools to the CNF to compute satisfying
assignments.
- Decode the result into an assignment of first-order variables.
Documentation
=============
For installation, examples, tutorials, and so on, please see `online documentation <https://pygplib.readthedocs.io/en/latest/>`__ .
Citation
========
Please cite the following paper if you use ``pygplib``:
::
@INPROCEEDINGS {10356599,
author = {T. Toda and T. Ito and J. Kawahara and T. Soh and A. Suzuki and J. Teruyama},
booktitle = {2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)},
title = {Solving Reconfiguration Problems of First-Order Expressible Properties of Graph Vertices with Boolean Satisfiability},
year = {2023},
volume = {},
issn = {},
pages = {294-302},
abstract = {This paper presents a unified framework for capturing a variety of graph reconfiguration problems in terms of firstorder expressible properties and proposes a Boolean encoding for formulas in the first-order logic of graphs based on the exploitation of fundamental properties of graphs. We show that a variety of graph reconfiguration problems captured in our framework can be computed in a unified way by combining our encoding and Boolean satisfiability solver in a bounded model checking approach but allowing us to use quantifiers and predicates on vertices to express reconfiguration properties.},
keywords = {automata;model checking;encoding;artificial intelligence},
doi = {10.1109/ICTAI59109.2023.00050},
url = {https://doi.ieeecomputersociety.org/10.1109/ICTAI59109.2023.00050},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {nov}
}
Bugs/Requests/Discussions
=========================
Please report bugs and requests from `GitHub Issues
<https://github.com/toda-lab/pygplib/issues>`__ , and
ask questions from `GitHub Discussions <https://github.com/toda-lab/pygplib/discussions>`__ .
History
=======
Please see `CHANGES <https://github.com/toda-lab/pygplib/blob/main/CHANGES.rst>`__ .
License
=======
Please see `LICENSE <https://github.com/toda-lab/pygplib/blob/main/LICENSE>`__ .
.. |Test| image:: https://github.com/toda-lab/pygplib/actions/workflows/test.yml/badge.svg
:target: https://github.com/toda-lab/pygplib/actions/workflows/test.yml
.. |Coverage| image:: https://codecov.io/gh/toda-lab/pygplib/graph/badge.svg?token=WWR54JE3M1
:target: https://codecov.io/gh/toda-lab/pygplib
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:target: https://pypi.org/project/pygplib/
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