7 citations · 18 across the 12 of their papers we have counts for
13 papers
HIF: The hypergraph interchange format for higher-order networks
Martín Coll, Cliff A. Joslyn, Nicholas W. Landry +5
Many empirical systems contain complex interactions of arbitrary size, representing, for example, chemical reactions, social groups, co-authorship relationships, and ecological dep…
HyperNetX: A Python package for modeling complex network data as hypergraphs
Brenda Praggastis, Sinan Aksoy, Dustin Arendt +5
HyperNetX (HNX) is an open source Python library for the analysis and visualization of complex network data modeled as hypergraphs. Initially released in 2019, HNX facilitates expl…
Experimental Observations of the Topology of Convolutional Neural Network Activations
Emilie Purvine, Davis Brown, Brett Jefferson +6
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of compl…
The SVD of Convolutional Weights: A CNN Interpretability Framework
Brenda Praggastis, Davis Brown, Carlos Ortiz Marrero +3
Deep neural networks used for image classification often use convolutional filters to extract distinguishing features before passing them to a linear classifier. Most interpretabil…
High-order Line Graphs of Non-uniform Hypergraphs: Algorithms, Applications, and Experimental Analysis
Xu T. Liu, Jesun Firoz, Sinan Aksoy +5
Hypergraphs offer flexible and robust data representations for many applications, but methods that work directly on hypergraphs are not readily available and tend to be prohibitive…
Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response
Song Feng, Emily Heath, Brett Jefferson +25
Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular d…
Parallel Algorithms and Heuristics for Efficient Computation of High-Order Line Graphs of Hypergraphs
Xu T. Liu, Jesun Firoz, Andrew Lumsdaine +4
This paper considers structures of systems beyond dyadic (pairwise) interactions and investigates mathematical modeling of multi-way interactions and connections as hypergraphs, wh…
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
Loc Truong, Chace Jones, Brian Hutchinson +5
Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning…
Hypernetwork Science: From Multidimensional Networks to Computational Topology
Cliff A. Joslyn, Sinan Aksoy, Tiffany J. Callahan +5
As data structures and mathematical objects used for complex systems modeling, hypergraphs sit nicely poised between on the one hand the world of network models, and on the other t…
A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information
Cliff Joslyn, Lauren Charles, Chris DePerno +7
Integration of heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations f…
Hypernetwork Science via High-Order Hypergraph Walks
Sinan G. Aksoy, Cliff Joslyn, Carlos Ortiz Marrero +2
We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative, yielding h…
Local homology of abstract simplicial complexes
Michael Robinson, Chris Capraro, Cliff Joslyn +4
This survey describes some useful properties of the local homology of abstract simplicial complexes. Although the existing literature on local homology is somewhat dispersed, it is…
Maximal Sections of Sheaves of Data over an Abstract Simplicial Complex
Brenda Praggastis
We employ techniques from topological data analysis to model sensor networks. Our approach to sensor integration uses the topological method of sheaves over cell complexes. The int…