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Found 7 papers, 4 papers with code
Explaining the Power of Topological Data Analysis in Graph Machine Learning
However, claims regarding the power and usefulness of TDA have only been partially tested in application domains where TDA-based models are compared to other graph machine learning approaches, such as graph neural networks.
Vector Summaries of Persistence Diagrams for Permutation-based Hypothesis Testing
In this context, one of the earliest works on hypothesis testing focuses on the two-group permutation-based approach where the associated loss function is defined in terms of within-group pairwise bottleneck or Wasserstein distances between persistence diagrams (Robinson and Turner, 2017).
A fast topological approach for predicting anomalies in time-varying graphs
In the past decade, a persistence diagram (PD) from topological data analysis (TDA) has become a popular descriptor of shape of data with a well-defined distance between points.
A computationally efficient framework for vector representation of persistence diagrams
In Topological Data Analysis, a common way of quantifying the shape of data is to use a persistence diagram (PD).
Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world.
Harnessing the power of Topological Data Analysis to detect change points in time series
While the applications of topological data analysis to change point detection are potentially very broad, in this paper we primarily focus on integrating topological concepts with the existing nonparametric methods for change point detection.
Unsupervised Space-Time Clustering using Persistent Homology
This paper presents a new clustering algorithm for space-time data based on the concepts of topological data analysis and in particular, persistent homology.
