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Found 6 papers, 4 papers with code
On the effectiveness of persistent homology
The goal of this work is to identify some types of problems where PH performs well or even better than other methods in data analysis.
Weisfeiler and Lehman Go Cellular: CW Networks
Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).
Ranked #1 on
Graph Regression
on ZINC 100k
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.
Can neural networks learn persistent homology features?
Topological data analysis uses tools from topology -- the mathematical area that studies shapes -- to create representations of data.
Stratifying multiparameter persistent homology
A fundamental tool in topological data analysis is persistent homology, which allows extraction of information from complex datasets in a robust way.
Algebraic Topology Commutative Algebra 55B55, 68U05, 68Q17, 13P25 (primary)
A roadmap for the computation of persistent homology
We give a friendly introduction to PH, navigate the pipeline for the computation of PH with an eye towards applications, and use a range of synthetic and real-world data sets to evaluate currently available open-source implementations for the computation of PH.
Algebraic Topology Computational Geometry Data Analysis, Statistics and Probability Quantitative Methods
