Tutorial 1: What is Geometric Deep Learning?
Introduction to GDL concepts.
A comprehensive guide to Graph Neural Networks
Introduction to GDL concepts.
Getting started with PyTorch.
Understanding GAT architecture.
Spectral graph convolutions explained.
How aggregation works in GNNs.
GAE and VGAE architectures.
ARGA and ARVGA models.
Generating graphs with deep learning.
RGNNs explained.
Theoretical foundations of node embeddings.
Implementation of node embeddings.
Analyzing edges in graphs.
Heterogeneous graph embeddings.
Working with datasets.
Advanced data handling.
Creator of PyG.
Graph ML Research.
Hierarchical graph pooling.