GRandMa (2022 - 2026) aims to fill this gap, by incorporating the use of Random Graphs in modern Graph ML algorithms. By improving our theoretical understanding of these algorithms and their limitations on large graphs, we hope to develop new, efficient graph ML algorithms.
- Spring 2022: Master 2 Internship + PhD (Filled): Random Graphs in Machine Learning. The core topic of GRandMa: the use of random graphs to study generalization and sample complexities in graph machine learning. W/ Simon Barthelmé and Yohann De Castro.
- N. Keriven. Not too little, not too much: a theoretical analysis of graph (over)smoothing NeurIPS 2022. Pdf
- N. Keriven. Entropic Optimal Transport on Random Graphs Preprint 2022. Pdf
- N. Keriven, A. Bietti, S. Vaiter. On the Universality of Graph Neural Networks on Large Random Graphs NeurIPS 2021. Pdf
- N. Keriven, A. Bietti, S. Vaiter. Convergence and Stability of Graph Convolutional Network Networks on Large Random Graphs NeurIPS (Spotlight) 2020. Pdf