42 citations · 107 across the 3 of their papers we have counts for
8 papers
Continuous-in-Depth Neural Networks
Alejandro F. Queiruga, N. Benjamin Erichson, Dane Taylor +1
Recent work has attempted to interpret residual networks (ResNets) as one step of a forward Euler discretization of an ordinary differential equation, focusing mainly on syntactic…
Multiplex Markov Chains: Convection Cycles and Optimality
Dane Taylor
Multiplex networks are a common modeling framework for interconnected systems and multimodal data, yet we still lack fundamental insights for how multiplexity affects stochastic pr…
Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling
Dane Taylor, Mason A. Porter, Peter J. Mucha
We describe centralities in temporal networks using a supracentrality framework to study centrality trajectories, which characterize how the importances of nodes change in time. We…
Synchronization of Network-Coupled Oscillators with Uncertain Dynamics
Per Sebastian Skardal, Dane Taylor, Jie Sun
Synchronization of network-coupled dynamical units is important to a variety of natural and engineered processes including circadian rhythms, cardiac function, neural processing, a…
Tunable Eigenvector-Based Centralities for Multiplex and Temporal Networks
Dane Taylor, Mason A. Porter, Peter J. Mucha
Characterizing the importances (i.e., centralities) of nodes in social, biological, and technological networks is a core topic in both network science and data science. We present…
Post-processing partitions to identify domains of modularity optimization
William H. Weir, Scott Emmons, Ryan Gibson +2
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of…