#graphNeuralNetworks #geometricDeepLearning #graphConvolutionalNetworks
The video PDF note is downloadable at
Lecture 10 is a brief introduction to geometric deep learning: an exciting research field intersecting graph theory and and deep learning.
In this lecture, I cover the three fundamental rules driving the field of deep learning including:
1) Locality: “tell me who your neighbours are, I will tell you who you are”,
2) Aggregation: “how to integrate information or messages you get from your neighbour?”, and
3) Composition: “how deep you want to learn from your neighbours’ messages?”
**** Resources and further readings ****
1. Stanford course “CS224W: Machine Learning with Graphs”, offered by Jure Leskovec:
A special thanks to my students Alin Banka and Inis Buzi for sharing this with me 🙂
2. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C. and Yu, P.S., 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596.
3. Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C. and Sun, M., 2018. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434.
4. Graph-based deep learning literature in top conferences: