After having seen the data, we can implement a simple graph neural network. The GNN applies a sequence of graph layers (GCN, GAT, or GraphConv), ReLU as activation function, and dropout for regularization. See below for the specific implementation.

In this paper, we propose DNNPerf, a novel ML-based tool for predicting the runtime performance of DL models with Graph Neural Network (GNN) [21]. Our key observation is that a DL model can be represented as a directed acyclic computation graph [22].

图神经网络(GNN)简介 . 主讲人：kiyotakali. GNN能做什么(和CNN相比) . 近十年来(从2012年AlexNet开始计算)，深度学习在计算机视觉(CV)和自然语言处理(NLP)等领域得到的长足的发展，深度神经网络对于图像和文字等欧几里得数据(Euclidean data)可以进行较好的处理

卷积神经网络 (CNN)能够利用图像数据的移位不变性、局部连通性和组合性。 因此，CNNs可以提取局部有意义的特征，并与整个数据集共享，用于各种图像分析。 虽然深度学习有效地捕获了欧几里得数据的隐藏模式，但数据以图形形式表示的应用越来越多。 例如，在电子商务中，以图形为基础的学习系统可以利用用户与产品之间的互动，作出高度准确的推荐；在化学中，分子被建模为图形，他们的生物活性需要被识别为药物发现；在引文网络中，论文通过引文相互链接，需要被 …

GNN fundamentals, modeling, applications, complexity, algorithms, accelerators & data flows: A review of the field of GNNs is presented from a computing perspective. A brief tutorial is included on GNN fundamentals, alongside an in-depth analysis of acceleration schemes, culminating in a communication-centric vision of GNN accelerators.

2023年8月17日 · We’ll describe Graph Neural Networks (GNNs), cover popular GNN libraries, and we’ll finish with great learning resources to get you started in this field. Prerequisites: This article assumes a basic understanding of Machine Learning (ML), Deep Learning (DL), and …

A brief tutorial is included on GNN fundamentals, alongside an in-depth analysis of acceleration schemes, culminating in a communication-centric vision of GNN accelerators. While other works provide comprehensive overviews of the field, our work focuses on explaining and illustrating key GNN techniques to the AI practitioner.

To ease the process, DGl-Go is a command-line interface to get started with training, using and studying state-of-the-art GNNs. DGL collects a rich set of example implementations of popular GNN models of a wide range of topics. Researchers can search for related models to innovate new ideas from or use them as baselines for experiments.

We introduce a methodology to explain GNNs using Class Expressions (CE). Our approach is designed to work on heterogeneous datasets with diverse node types, edge types, and node features. The core idea is to leverage Class Expressions (CE) from the description logic EL, which provides support for intersections and exist-relations.

2022年3月20日 · Graph Deep Learning (GDL) has picked up its pace over the years. The natural network-like structure of many real-life problems makes GDL a versatile tool in the shed. The field has shown a lot of promise in social media, drug-discovery, chip placement, forecasting, bioinformatics, and more.