Search Results for "graph theory"

...Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python.

Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the dataset to include over 520,000 materials within 1 meV/atom of the convex hull as of August 2024. ...

...The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side.

This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested...

...It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible.

...However, it is hard to detect similarity in source code especially if the number of students is high. Furthermore, it is better to prevent plagiarism before it is committed. The “Anti-Plagiarism Graph Generator” program is developed to prevent plagiarism in Graph Theory course programming assignments. The prevention is done at the very beginning before the students start to do their programming assignments. This is possible as students will get a different set of question based on their students’ identification number. Students will generate their own graph by using this program. ...

xPyder is a PyMOL plugin to analyze and visualize on the 3D structure dynamical cross-correlation matrices (DCCM), linear mutual information (LMI), communication propensities (CP), intra- and inter-molecular interactions (e.g. PSN), and more, to produce highly customizable publication-quality images. xPyder identifies networks (using concepts from graph theory, such as hubs and shortest path searching), compares matrices and focuses the analysis on relevant information by filtering the data using a modular, user-expandable plugin system that takes advantage of structural and dynamical information, contributing to bridge the gap between dynamical and mechanical properties at the molecular level.

pyGraph is tool that hopefully will help graph theory students by analizing,transforming and creating graphs Dependencies: python and pygame

This application is designed to solve problems of transshipment. This problem of the graph theory consists in finding a negative stream on the arcs which minimizes the total cost of the transport in a network R(V,E,b,c) where : - V = vertex - E