Uma Introdução Amigável às Redes Neurais para Grafos

Abstract

Graph neural networks have driven a series of recent developments in, e.g., drug discovery, recommender systems, and social network analysis. At their core, GNNs are designed to extract numerical representations for each node in a graph, recursively combining representations of neighboring nodes. This tutorial paper covers some popular and influential GNN models, and discusses their applications in different disciplines. We hope this work will help popularize GNNs in the local community, and foster scientific advances in machine learning and data science.