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Found 14 papers, 6 papers with code
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
In this research, we study perceptual variability in conducting visual clustering, which we call Cluster Ambiguity.
Topological Deep Learning: Going Beyond Graph Data
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations.
Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment
Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial.
Leveraging Peer Review in Visualization Education: A Proposal for a New Model
Based on the literature review from the field of Writing Studies, this paper proposes a new framework to implement visualization peer review in the classroom to engage today's students.
Human-Computer Interaction
LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts
We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks.
Human-Computer Interaction
Fast and Scalable Complex Network Descriptor Using PageRank and Persistent Homology
The PageRank of a graph is a scalar function defined on the node set of the graph which encodes nodes centrality information of the graph.
Topologically-Guided Color Image Enhancement
Enhancement is an important step in post-processing digital images for personal use, in medical imaging, and for object recognition.
Mesh Learning Using Persistent Homology on the Laplacian Eigenfunctions
We use persistent homology along with the eigenfunctions of the Laplacian to study similarity amongst triangulated 2-manifolds.
An Efficient Data Retrieval Parallel Reeb Graph Algorithm
That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure.
Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs
Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints.
Parallel Mapper
The construction of Mapper has emerged in the last decade as a powerful and effective topological data analysis tool that approximates and generalizes other topological summaries, such as the Reeb graph, the contour tree, split, and joint trees.
The Shape of an Image: A Study of Mapper on Images
We study the topological construction called Mapper in the context of simply connected domains, in particular on images.
