How to use edge features in Graph Neural Networks (and PyTorch Geometric)

In this video I talk about edge weights, edge types and edge features and how to include them in Graph Neural Networks. :) ▬▬ Papers ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Edge types: - Modeling Relational Data with Graph Convolutional Network (https://arxiv.org/pdf/1703.06103.pdf) - GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation (https://arxiv.org/pdf/1906.12192.pdf) Multidim. edge features: - Neural Message Passing for Quantum Chemistry (https://arxiv.org/pdf/1704.01212.pdf) - Principal Neighbourhood Aggregation for Graph Nets (https://arxiv.org/pdf/2004.05718.pdf) - Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties (https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301) Edge feature embeddings: - NENN: Incorporate Node and Edge Features in Graph Neural Networks (http://proceedings.mlr.press/v129/yang20a.html) - Exploiting Edge Features in Graph Neural Networks (https://arxiv.org/pdf/1809.02709.pdf) ▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬ 00:00 Introduction 05:10 Edge weights 06:10 Edge types / relations 09:21 Multidim. edge features 12:04 Edge feature embeddings 13:52 Pytorch Geometric ▬▬ Support me if you like 🌟 ►Link to this channel: https://bit.ly/3zEqL1W ►Support me on Patreon: https://bit.ly/2Wed242 ►Buy me a coffee on Ko-Fi: https://bit.ly/3kJYEdl