GraSPy: Graph Statistics in Python

2 code implementations • 29 Mar 2019 • Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein

We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations.

BIG-bench Machine Learning

AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python

1 code implementation • 6 Sep 2019 • Thomas L. Athey, Tingshan Liu, Benjamin D. Pedigo, Joshua T. Vogelstein

Background: Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation.

Clustering Density Estimation

Discovery of Multi-Level Network Differences Across Populations of Heterogeneous Connectomes

1 code implementation • 30 Nov 2020 • Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford, Benjamin D. Pedigo, Jesús Arroyo, Lucy Upchurch, G. Allan Johnson, Nian Wang, Youngser Park, Carey E. Priebe, Joshua T. Vogelstein

A connectome is a map of the structural and/or functional connections in the brain.

Leveraging semantically similar queries for ranking via combining representations

no code implementations • 23 Jun 2021 • Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo, Shweti Mahajan, Vince Lyzinski, Youngser Park, Amitabh Basu, Piali~Choudhury, Christopher M. White, Weiwei Yang, Carey E. Priebe

In modern ranking problems, different and disparate representations of the items to be ranked are often available.

Learning-To-Rank

Graph Matching via Optimal Transport

1 code implementation • 9 Nov 2021 • Ali Saad-Eldin, Benjamin D. Pedigo, Carey E. Priebe, Joshua T. Vogelstein

The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements.

Graph Matching