The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning have become one of the fastest-growing research topics in machine learning, especially deep learning.
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible ...
Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009). To learn more about graph networks, see our arXiv paper: ...
15.01.2021 · Graph Neural Networks(GNN) are a powerful tool for solving problems on graph-structured inputs. GNNs are a super exciting sub-area of machine learning that is getting a lot of attention and activity and some impressive results recently.Google team recently used the molecular structure of compounds along with GNNs to predict their aromaand showed that, …
Graph Neural Networks Libraries. Deep Graph Library (DGL). A Python package that interfaces between existing tensor libraries and data being expressed as graphs ...
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform. - GitHub - tensorflow/gnn: TensorFlow GNN is a library to build Graph ...
Graph Neural Networks (GNNs) I A type of neural networks operating directly on graphs [1]. I To learn a state representation which contains information of each vertex’s neighborhood. I Notations in this tutorial Notation Description Rm m …
26 Graph Neural Networks in Anomaly Detection 561 26.2 Issues In this section, we provide a brief discussion and summary of the issues in GNN-based anomaly detection. In particular, we group them into three: (i) data-specific issues, (ii) task …
Efficient Graph Neural Networks - a curated list of papers and projects - GitHub - chaitjo/awesome-efficient-gnn: Efficient Graph Neural Networks - a ...
Class GitHub Graph Neural Networks. In the previous section, we have learned how to represent a graph using “shallow encoders”. Those techniques give us powerful expressions of a graph in a vector space, but there are limitations as well.
Benchmark Dataset for Graph Classification: This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks by Filippo Bianchi. GAM: A PyTorch implementation of “Graph Classification Using Structural Attention” (KDD 2018) by Benedek Rozemberczki.