Concept
Graph rewriting
Related publications (315)
Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
Ksenia Briling, Puck Elisabeth van Gerwen, Yannick Calvino Alonso, Malte Martin Franke
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...
Royal Soc Chemistry2024Post stroke recovery prediction
Dimitri Nestor Alice Van De Ville, Elvira Pirondini, Cyprien Alban Félicien Rivier
The present relates to a method for generating a graph representing a virtually lesioned connectome of a patient after a stroke comprising:- providing structural data of the brain of the patient after the stroke;- segmenting said structural data to delinea ...
2024The Power of Two Matrices in Spectral Algorithms for Community Recovery
Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...
Ieee-Inst Electrical Electronics Engineers Inc2024The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al.
We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...
Elsevier2024Molecular hypergraph neural networks
Philippe Schwaller, Junwu Chen
Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...
Aip Publishing2024Equivariant Neural Architectures for Representing and Generating Graphs
Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...
EPFL2023A tutorial on graph models for scheduling round-robin sports tournaments
Many sports leagues organize their competitions as round-robin tournaments. This tournament design has a rich mathematical structure that has been studied in the literature over the years. We review some of the main properties and fundamental scheduling me ...
WILEY2023Electricity Theft Detection Using Dynamic Graph Construction and Graph Attention Network
Wenlong Liao, Bin Zhang, Zhe Yang, Bin Feng
The integrations of advanced metering infrastructure and smart meters make it possible to detect electricity thieves by analyzing electricity consumption readings. However, the detection accuracies of traditional models are limited due to their difficulty ...
Piscataway2023Privatized graph federated learning
Ali H. Sayed, Stefan Vlaski, Elsa Rizk
Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...
SPRINGER2023Equilibria in Network Constrained Energy Markets
We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...
Elsevier2023