Graph Convolutional Networks: Introduction to GNNs

Graph Neural Networks (GNNs) represent one of the most captivating and rapidly evolving architectures within the deep learning landscape. As deep learning models designed to process data structured as graphs, GNNs bring remarkable versatility and powerful learning capabilities. Among the various types of GNNs, the Graph Convolutional Networks (GCNs) have emerged as the most <a href="https://paperswithcode.com/methods/category/graph-models" rel="noopener ugc nofollow" target="_blank">prevalent and broadly applied model</a>. GCNs are innovative due to their ability to leverage both the features of a node and its locality to make predictions, providing an effective way to handle graph-structured data. In this article, we will delve into the mechanics of the GCN layer and explain its inner workings. Furthermore, we will explore its practical application for node classification tasks, using <a href="https://pytorch-geometric.readthedocs.io/en/latest/index.html" rel="noopener ugc nofollow" target="_blank">PyTorch Geometric</a> as our tool of choice. PyTorch Geometric is a specialized extension of PyTorch that has been created specifically for the development and implementation of GNNs. It is an advanced, yet user-friendly library that provides a comprehensive suite of tools to facilitate graph-based machine learning. To commence our journey, the PyTorch Geometric installation will be required. If you are using Google Colab, <a href="https://pytorch.org/get-started/locally/" rel="noopener ugc nofollow" target="_blank">PyTorch</a> should already be in place, so all we need to do is execute a few additional commands. <a href="https://towardsdatascience.com/graph-convolutional-networks-introduction-to-gnns-24b3f60d6c95">Visit Now</a>