32 citations · 71 across the 14 of their papers we have counts for
5 papers · 1 filter
Graph Model Selection via Random Walks
Lin Li, William M. Campbell, Rajmonda S. Caceres
In this paper, we present a novel approach based on the random walk process for finding meaningful representations of a graph model. Our approach leverages the transient behavior o…
A supervised approach to time scale detection in dynamic networks
Benjamin Fish, Rajmonda S. Caceres
For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing…
Model Selection Framework for Graph-based data
Rajmonda S. Caceres, Leah Weiner, Matthew C. Schmidt +2
Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the gener…
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…
Handling oversampling in dynamic networks using link prediction
Benjamin Fish, Rajmonda S. Caceres
Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far t…