Universal Function Approximation on Graphs

Rickard Brüel-Gabrielsson

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arXiv · 2003.06706

Universal Function Approximation on Graphs

Rickard Brüel-Gabrielsson

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Abstract

In this work we produce a framework for constructing universal function approximators on graph isomorphism classes. We prove how this framework comes with a collection of theoretically desirable properties and enables novel analysis. We show how this allows us to achieve state-of-the-art performance on four different well-known datasets in graph classification and separate classes of graphs that other graph-learning methods cannot. Our approach is inspired by persistent homology, dependency parsing for NLP, and multivalued functions. The complexity of the underlying algorithm is O(#edges x #nodes) and code is publicly available (https://github.com/bruel-gabrielsson/universal-function-approximation-on-graphs).

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BibTeX RIS

Rickard Brüel-Gabrielsson. 2020-03-14. Universal Function Approximation on Graphs. https://arxiv.org/abs/2003.06706

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