Filtration Curves for Graph Representation
Leslie O’Bray, Bastian Rieck, Karsten Borgwardt
Abstract
The two predominant approaches to graph comparison in recent years are based on (i) enumerating matching subgraphs or (ii) comparing neighborhoods of nodes. In this work, we complement these two perspectives with a third way of representing graphs: using filtration curves from topological data analysis that capture both edge weight information and global graph structure. Filtration curves are highly efficient to compute and lead to expressive representations of graphs, which we demonstrate on graph classification benchmark datasets. Our work opens the door to a new form of graph representation in data mining.
Topics & Concepts
GraphComputer scienceComplement (music)Representation (politics)Theoretical computer scienceExternal Data RepresentationMatching (statistics)CombinatoricsMathematicsArtificial intelligenceStatisticsPhenotypePolitical scienceBiochemistryLawComplementationChemistryPoliticsGeneTopological and Geometric Data AnalysisAdvanced Graph Neural NetworksComplex Network Analysis Techniques