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I'm new to the area, and we don't have a course on graph neural networks at our university. However, I will still like to know the main theoretical results when considering convergence of graph neural networks. Where can I learn more about the topic? Thanks.

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More generally, this area is known as geometric deep learning, i.e., it encompasses learning not only on graph but on other non-Euclidean domains. A good broad start is the survey of Bronstein et al. [1], after which I can recommend basically any material by Bronstein like his many excellent presentations (also available on Youtube - just do a search).

For graph neural nets, you might start with Kipf & Welling (the survey [1] will give references). Perhaps loosely building on their work, there is also GraphSAGE [2] and later developments like graph attention networks. I've not actively followed on the literature for the past 2-3 years, so I don't know what's hot currently or how badly outdated these methods might be, but these works definitely will give you the foundation.


[1] Bronstein, Michael M., Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. "Geometric deep learning: going beyond Euclidean data." IEEE Signal Processing Magazine 34, no. 4 (2017): 18-42.

[2] Hamilton, William L., Rex Ying, and Jure Leskovec. "Inductive representation learning on large graphs." In Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025-1035. 2017.

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  • $\begingroup$ Thanks! Are there materials that are structured like a course, or a series of videos? $\endgroup$
    – nir shahar
    Jul 5 at 10:45
  • $\begingroup$ @nirshahar The ones I'm thinking of are more like conference (keynote) talks, so high level intros to main results etc. $\endgroup$
    – Juho
    Jul 5 at 12:25

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