Jan 3, 2023 · In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks.

Dec 11, 2018 · Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs.

By far the cleanest and most elegant library for graph neural networks in PyTorch. Highly recommended! Unifies Capsule Nets (GNNs on bipartite graphs) and Transformers (GCNs with attention on fully-connected graphs) in a single API.

Graphs are data structures that can be ingested by various algorithms, notably neural nets, learning to perform tasks such as classification, clustering and regression. TL;DR: here’s one way to make graph data ingestable for the algorithms: Algorithms can “embed” each node of a graph into a real vector (similar to the embedding of a word).

Speci cally, we propose the GraphEDM framework, which generalizes popular algorithms for semi-supervised learning (e.g. GraphSage, GCN, GAT), and unsupervised learning (e.g. DeepWalk, node2vec) of graph representations into a single consistent ap-proach.

Feb 21, 2019 · Deep learning on graphs is taking more importance by the day. Here I’ll show the basics of thinking about machine learning and deep learning on graphs with the library Spektral and the platform MatrixDS.

Mar 14, 2025 · In this article, we explore Graph Machine Learning, its applications, benefits, and how it is shaping the future of AI and data science. What is Graph Machine Learning? Graph Machine Learning is a subset of AI that leverages graph structures to analyze and predict relationships between entities.

Jan 25, 2025 · By detailing how these methods can transform complex graph structures into machine-readable formats, we can better appreciate their role in contemporary machine learning.

Deep learning and machine learning using graphs are rapidly evolving fields that are transforming the way we approach data analysis and decision-making. This cheat sheet provides a comprehensive overview of the key concepts, topics, and categories related to these fields, including graph theory, deep learning, machine learning, graph neural ...

Apr 3, 2025 · Computational graphs are a type of graph that can be used to represent mathematical expressions. This is similar to descriptive language in the case of deep learning models, providing a functional description of the required computation.