This year's ICLM will finally have a tutorial on graphs! Adrian Arnaiz-Rodriguez and Ameya Velingker will present a tutorial on on Graph Learning: Principles, Challenges, and Open Directions.
🗓️ Date: Monday, July 22
🕒 Time: 15:30 CEST - 17:30 CEST
📍 ICML In-person Event: Hall A8, ICML Venue
📍 Virtual attendance: https://icml.cc/virtual/2024/tutorial/35233

What to expect?
- Intro to Graph Learning and GNNs: Introduction to Traditional graph representation, Graph Neural Networks (GNNs), Message Passing Networks (MPNNs), Graph Transformers (GTs) and spectral quantities.
- Expressiveness and Generalizability: GNN expressivity linked with the WL test, generalizability of MPNNs, and their performance implications.
- Challenges in GNNs: Understanding and addressing under-reaching, over-smoothing, over-squashing, and graph rewiring techniques.
- Panel Discussion on Future Directions: Panel discussion with Michael Bronstein, Bryan Perozzi, Christopher Morris and more panelist TBC. We will discuss about GNN limitations, graph foundation models, and integrating GNNs with large language models (LLMs).

This tutorial balances introductory content and advanced insights, aimed to both general audiences and experts. Don’t miss this opportunity to deepen your understanding of GNNs!