Related papers: Future Directions in the Theory of Graph Machine Learning

Future Directions in the Theory of Graph Machine Learning

URL: http://arxiv.org/abs/2402.02287v4
Date: Fri, 14 Jun 2024 15:54:12 GMT
Title: Future Directions in the Theory of Graph Machine Learning
Authors: Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, İsmail İlkan Ceylan, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka,
Abstract summary: Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data. Despite their practical success, our theoretical understanding of the properties of GNNs remains highly incomplete.
Score: 49.049992612331685
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 a broad spectrum of disciplines, from life to social and engineering sciences. Despite their practical success, our theoretical understanding of the properties of GNNs remains highly incomplete. Recent theoretical advancements primarily focus on elucidating the coarse-grained expressive power of GNNs, predominantly employing combinatorial techniques. However, these studies do not perfectly align with practice, particularly in understanding the generalization behavior of GNNs when trained with stochastic first-order optimization techniques. In this position paper, we argue that the graph machine learning community needs to shift its attention to developing a balanced theory of graph machine learning, focusing on a more thorough understanding of the interplay of expressive power, generalization, and optimization.

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