Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep ...

1 Computer Science Department, University of Pisa, Pisa, Italy; 2 Scuola Normale Superiore, Pisa, Italy; In this work, we study the phenomenon of catastrophic forgetting in the graph representation ...

This repository contains a PyTorch implementation of a deep learning based graph-transformer for whole slide image (WSI) classification. We propose a Graph-Transformer (GT) network that fuses a graph ...

After extracting the graph features, we developed four main classes of deep learning models for EEG classification. The first one is MLP ( Basha et al., 2020 ) which provides a simple and ...

Graphs are used to model complex systems, and GNNs provide a way to analyze and make predictions based on the structure of the graph. GNNs are a type of deep-learning algorithm that can operate on ...

Official repository for survey paper "Deep Graph Anomaly Detection: A Survey and New Perspectives& quot ... Imbalanced Node Classification on Graphs with Graph Neural Networks in WSDM, 2021 ...

Abstract: Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of ...