Title
Filtration Curves for Graph Representation
Abstract
ABSTRACTThe 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.
Year
DOI
Venue
2021
10.1145/3447548.3467442
Knowledge Discovery and Data Mining
Keywords
DocType
Citations 
Graph classification, graph representation
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Leslie O'Bray121.50
Bastian Rieck25110.10
Karsten M. Borgwardt32799155.36