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Found 6 papers, 4 papers with code
Topological Learning for Motion Data via Mixed Coordinates
Topology can extract the structural information in a dataset efficiently.
Exact and rapid linear clustering of networks with dynamic programming
We study the problem of clustering networks whose nodes have imputed or physical positions in a single dimension, for example prestige hierarchies or the similarity dimension of hyperbolic embeddings.
Nerve theorems for fixed points of neural networks
In this work, we propose a method of covering graphs of CTLNs with a set of smaller directional graphs that reflect the local flow of activity.
Generalized Penalty for Circular Coordinate Representation
We provide simulation experiments and real data analysis to support our claim that circular coordinates with generalized penalty will detect the change in high-dimensional datasets under different sampling schemes while preserving the topological structures.
Construction of and efficient sampling from the simplicial configuration model
Simplicial complexes are now a popular alternative to networks when it comes to describing the structure of complex systems, primarily because they encode multi-node interactions explicitly.
Physics and Society Statistics Theory Methodology Statistics Theory
Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates
This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system.
