222 citations · 1.3k across the 45 of their papers we have counts for
6 papers · 1 filter
A tensor factorization model of multilayer network interdependence
Izabel Aguiar, Dane Taylor, Johan Ugander
Multilayer networks describe the rich ways in which nodes are related by accounting for different relationships in separate layers. These multiple relationships are naturally repre…
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…
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…
Super-resolution community detection for layer-aggregated multilayer networks
Dane Taylor, Rajmonda S. Caceres, Peter J. Mucha
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. Fo…
Clustering Network Layers With the Strata Multilayer Stochastic Block Model
Natalie Stanley, Saray Shai, Dane Taylor +1
Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gi…