pygplib 2.2.0

Python First-Order Graph Property Library

Python First-Order Graph Property Library

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 .

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 , and ask questions from GitHub Discussions .

History

Please see CHANGES .

License

Please see LICENSE .