Diego Kozlowski: Machine Learning on graphs

Machine Learning Seminar presentation

Topic: Machine Learning on graphs

Speaker: Diego Kozlowski, University of Luxembourg, Department of Computational Science

Time: Wednesday 2020.11.18, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks where observations are not independently drawn from the data generating process, but their codependencies add valuable information, a network analysis might be useful for modelling those relations.

In this seminar, we will discuss Graph Neural Networks, the deep learning approach for dealing with networks.

 

Additional material:

[1] Hamilton, W. L. (2020). Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 14(3), 1-159. (https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf)

[2] Bacciu, D., Errica, F., Micheli, A., & Podda, M. (2020). A gentle introduction to deep learning for graphs. Neural Networks. (https://arxiv.org/abs/1912.12693)

[3] Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv preprint (https://arxiv.org/abs/1709.05584)

[4] Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 18-42. (https://arxiv.org/abs/1611.08097)