Tutorial 1: What is Geometric Deep Learning?
Introduction to GDL concepts.
Join the session 2.0 :)
Tutorial 2: PyTorch basics
Getting started with PyTorch.
Tutorial 3: Graph Attention Network GAT
Understanding GAT architecture.
Tutorial 4: Convolutional Layers - Spectral methods
Spectral graph convolutions explained.
Tutorial 5: Aggregation Functions in GNNs
How aggregation works in GNNs.
Tutorial 6: Graph Autoencoder
GAE and VGAE architectures.
Tutorial 7: Adversarial Regularizer Autoencoders
ARGA and ARVGA models.
Tutorial 8: Graph Generation
Generating graphs with deep learning.
Tutorial 9: Recurrent Graph Neural Networks
RGNNs explained.
Tutorial 10: DeepWalk and Node2Vec THEORY
Theoretical foundations of node embeddings.
Tutorial 11: DeepWalk and Node2Vec PRACTICE
Implementation of node embeddings.
Tutorial 12: Edge Analysis
Analyzing edges in graphs.
Tutorial 13: MetaPath2vec
Heterogeneous graph embeddings.
Tutorial 14: Data handling in PyG (Part 1)
Working with datasets.
Tutorial 15: Data handling in PyG (Part 2)
Advanced data handling.
Tutorial 16: Special Guest: Matthias Fey
Creator of PyG.
Tutorial 17: Special Guest: Sergei Ivanov
Graph ML Research.
Tutorial 18: Graph pooling: DIFFPOOL
Hierarchical graph pooling.