In this work we produce a framework for constructing universal function approximators on graph isomorphism classes. Additionally, we prove how this framework comes with a collection of theoretically desirable properties and enables novel analysis. We show how this allows us to outperform state of the art on four different well known datasets in graph classification and how our method can separate classes of graphs that other graph-learning methods cannot. Our approach is inspired by persistence homology, dependency parsing for Natural Language Processing , and multivalued functions. The complexity of the underlying algorithm is O(mn) and code is publicly available.
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(mn) and code is publicly available.
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Additionally, we
We
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outperform state of the art
achieve state-of-the-art performance
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well known
well-known
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how our method can
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persistence
persistent
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Natural Language Processing
NLP