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GRL Series
Understanding the Building Blocks of Graph Neural Networks (Intro)
Intuitions (with running code) on the neural framework for analyzing and learning from graph data
This post is an introduction to a series of articles on Graph Neural Networks (GNNs). The goal of this series is to provide a detailed description, with intuitions and examples, of the GNNs building blocks.
In this series, I will also share running code, using Numpy, Pytorch, and the most prominent libraries adopted in this field, such as Deep Graph Library (DGL) and Pytorch Geometric. At the end of this series, you will be able to combine these building blocks and create a neural architecture to perform analysis and learning tasks on graph data.
This series will analyze the main components to set up a GNN, including (i) the input layer, (ii) the GNN layer(s), and (iii) the Multilayer Perceptron (MLP) prediction layer(s).
The framework to analyze and decompose the standard GNN architectures is based on the recent paper entitled “Benchmarking Graph Neural Networks”, whose metadata is available below:
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., & Bresson, X. (2020). Benchmarking Graph Neural Networks. arXiv preprint arXiv:2003.00982.
Source: https://arxiv.org/abs/2003.00982
This post does not cover the fundamentals of graph theory and neural networks. For an…