Edutopia

The Power of Slow Reveal Graphs

One powerful way to do this is through a routine called slow reveal graphs.
IEEE

EasyDGL: Encode, Train and Interpret for Continuous-Time Dynamic Graph Learning

Abstract: Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics in continuous time domain for its flexibility. This paper aims to design an ...
IEEE

Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain Using Homogeneous Graph Neural Networks

Abstract: This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph ...
GitHub

Discrete Diffusion Schrödinger Bridge Matching

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometrics 2.3.1. Note that ${PROJECT_DIR} refers to this directory. The following section outlines the graph-to-graph transformation ...
GitHub

DeFoG: Discrete Flow Matching for Graph Generation

A PyTorch implementation of the DeFoG model for training and sampling discrete graph flows. (Please update to the latest version. Recent fixes have been applied ...
Frontiers

Hybrid heuristic approach for generalized police officer patrolling problem

1 Department of Life Science and Informatics, Graduate School of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan 2 Department of Life Engineering, Faculty of Engineering, ...