Machine Learning on graphs
Speaker: Diego Kozlowski (Faculty of Science, Technology and Medicine; University of Luxembourg)
Title: Machine Learning on graphs
Time: Wednesday, 2020.11.18, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
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 about Graph Neural Networks, the deep learning approach for dealing with networks.
Additional materials:
[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)