Course: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation ...

Deep Learning and Machine Learning has made breakthroughs in recent years. ... As a companion to this paper, we have released an open-source software library for building graph networks, with ...

Over the past few years the artificial intelligence community has shown an increasing interest in deep learning research on graph-structured data. Many neural network models on graphs — or graph ...

Deep learning techniques are used for data with an underlying non-Euclidean structure, such as graphs or manifolds, and are known as deep geometric learning. These techniques have previously been used ...

The course will help participants understand the need for node embeddings in setting up graphs for machine learning and talk about how neural networks are constructed and applied to graph data. For ...

PyTorch Geometric (PyG) remains one of the most used frameworks for geometric deep learning in 2024. It is a versatile solution that goes above PyTorch and provides the means to create Graph Neural ...

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Learning from graph and relational data plays a major role ...