2020/03/14 by Rickard Brüel-Gabrielsson, Rickard Brüel‐Gabrielsson, Brüel-Gabrielsson, Rickard
Computer Science · Immunology and Microbiology · Mathematics · #Advanced Graph Neural Networks #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #HIV Research and Treatment #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Topological and Geometric Data Analysis #cs.DS #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2003.06706
openalex publication_date 2020/03/14 · arxiv created 2020/10/26 · arxiv updated 2020/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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).