Abstract: [Add abstract here]

Abstract: Shaping GNNs with dynamical systems

The dynamics of information diffusion in local message passing is a key issue that heavily influences graph representation learning, especially when long-range propagation is needed. We vouch for principled approaches that control and regulate the degree of propagation, conservation and dissipation of information throughout the neural flow. In the talk we explore some approaches stemming from a dynamical systems' view of neural networks for static and temporal graphs, with particular focus on wide applicability of the concepts and on the theoretical guarantees on information conservation.

Title: Introduction to PyTorch Geometric 📐

This coding session will cover the basics of PyTorch Geometric, including data handling, model building, and training of graph neural networks. Students will get hands-on experience with key concepts and tools.

Title: Advanced PyTorch Geometric 📐 with Torch Geometric Pool 🎱

This coding session will cover advanced topics in PyTorch Geometric with the Torch Geometric Pool library, including pooling techniques and graph coarsening. Participants will learn how to implement and utilize these methods effectively to improve graph neural network performance.

Title: Graph Deep Learning for Time Series Forecasting

Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors leverage pairwise relationships by conditioning forecasts on graphs spanning the time series collection. This tutorial will explore these methods in depth, providing practical examples and insights.