IJCAI 2024poster0 citations
Towards a Theory of Machine Learning on Graphs and its Applications in Combinatorial Optimization
Abstract
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across many disciplines, from life and physical to social and engineering sciences. Despite their practical success, our theoretical understanding of the properties of GNNs remains incomplete. Here, we survey the author's and his collaborators' progress in developing a deeper theoretical understanding of GNNs' expressive power and generalization abilities. In addition, we overview recent progress in using GNNs to speed up solvers for hard combinatorial optimization tasks.
Machine Learning: ML: Representation learningMachine Learning: ML: Learning theoryMachine Learning: ML: Theory of deep learningConstraint Satisfaction and Optimization: CSO: Constraint optimization problems
BibTeX
@inproceedings{ijcai2024p981,
title = {Towards a Theory of Machine Learning on Graphs and its Applications in Combinatorial Optimization},
author = {Morris, Christopher},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8553--8558},
year = {2024},
month = {8},
note = {Early Career},
doi = {10.24963/ijcai.2024/981},
url = {https://doi.org/10.24963/ijcai.2024/981},
}