7 citations · 7 across the 1 of their papers we have counts for
6 papers
GraSPy: Graph Statistics in Python
Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford +3
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible a…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Randal Burns, Eric Perlman, Alex Baden +27
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visual…
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Gregory Kiar, Robert J. Anderson, Alex Baden +24
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capa…
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
Jordan Yoder, Li Chen, Henry Pao +5
Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realize…
Small-World Propensity in Weighted, Real-World Networks
Sarah Feldt Muldoon, Eric W. Bridgeford, Danielle S. Bassett
Quantitative descriptions of network structure in big data can provide fundamental insights into the function of interconnected complex systems. Small-world structure, commonly dia…