Announcing the Learning on Graphs Conference

As the field of machine learning has grown very rapidly, so too have its many subfields. In the last decade, machine learning on graphs has taken off, especially with impressive advances in approaches to graph deep learning in recent years. This has been a major boon to the application areas that process graph-structured data, such as computational chemistry, transportation networks, social networks, recommender systems, or healthcare. Graphs can also be seen as a generalisation of other domains (e.g., grids or sets), and using graph-based ML architectures where established models such as CNNs and RNNs have traditionally been used is often advantageous. However, growth also brings along a number of challenges.