17 citations · 27 across the 5 of their papers we have counts for
10 papers
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +3
This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State cluste…
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
Cong Ye, Konstantinos Slavakis, Pratik V. Patil +2
Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-n…
Cognitive chimera states in human brain networks
Kanika Bansal, Javier O. Garcia, Steven H. Tompson +3
The human brain is a complex dynamical system that gives rise to cognition through spatiotemporal patterns of coherent and incoherent activity between brain regions. As different r…
Optimizing state change detection in functional temporal networks through dynamic community detection
Michael Vaiana, Sarah F. Muldoon
Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However…
Resolution Limits for Detecting Community Changes in Multilayer Networks
Michael Vaiana, Sarah Muldoon
Multilayer networks capture pairwise relationships between the components of complex systems across multiple modes or scales of interactions. An important meso-scale feature of the…
Data-driven brain network models predict individual variability in behavior
Kanika Bansal, John D. Medaglia, Danielle S. Bassett +2
The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavi…