Sign upSign inSign upSign inAn archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.Member-only storyMichael BronsteinFollowTDS Archive–2ShareThis post was co-authored with Fabrizo Frasca and Emanuele Rossi.Graph Neural Networks (GNNs) are a class of ML models that have emerged in recent years for learning on graph-structured data. GNNs have been successfully applied to model systems of relation and interactions in a variety of different domains, including social science, computer vision and graphics, particle physics, chemistry, and medicine. Until recently, most of the research in the field has focused on developing new GNN models and testing them on small graphs (with Cora, a citation network containing only about 5K nodes, still being widely used [1]); relatively little effort has been invested in dealing with large-scale applications. On the other hand, industrial problems often deal with giant graphs, such as Twitter or Facebook social networks containing hundreds of millions of nodes and billions of edges. A big part of methods described in the literature are unsuitable for these settings.In a nutshell, graph neural networks operate by aggregating the features from local neighbour nodes. Arranging the d-dimensional node features into an n×d matrix X (here n denotes the number of nodes), the simplest convolution-like operation on graphs implemented in the popular GCN model [2] combines node-wise transformations with feature diffusion across adjacent nodesY = ReLU(AXW).Here W is a learnable matrix shared across all nodes and A is a linear diffusion operator amounting to a weighted average of features in a neighbourhood [3]. Multiple layers of this form can be applied in sequence like in traditional CNNs. Graph neural networks can be…—-2An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.DeepMind Professor of AI @Oxford. Serial startupper. ML for graphs, biochemistry, drug design, and animal communication.HelpStatusAboutCareersPressBlogPrivacyRulesTermsText to speech