Intro to Graph Neural Networks
Graph neural networks (GNNs) are a powerful new class of machine learning algorithms that are specifically designed to handle graph-structured data. Unlike traditional neural networks, which are designed to process data in the form of vectors or matrices, GNNs can operate directly on graphs, where nodes represent entities and edges represent relationships between entities. This makes them well-suited for a wide range of tasks that involve understanding the structure of complex systems, such as social networks, knowledge graphs, and molecular structures. In this session we will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the PyTorch Geometric (PyG) library. PyTorch Geometric is an extension library to the popular deep learning framework PyTorch, and consists of various methods and utilities to ease the implementation of Graph Neural Networks.