Search Results for author:
Found 5 papers, 1 papers with code
Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)
We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse summaries.
Quantifying the Controllability of Coarsely Characterized Networked Dynamical Systems
In contrast, in this paper the controllability aspects of the coarse system are derived from coarse summaries {\em without} knowledge of the fine-scale structure.
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
Our objective is to develop conditions on the graph structure, the quantity, and properties of measurements, under which we can recover the community organization in this coarse graph.
Node-Centric Graph Learning from Data for Brain State Identification
This is particularly true when the number of nodes is high or there are temporal changes in the network.
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals
Such a model maps signals onto vertices of a graph and the time-space dependencies among signals are modeled by the edge weights.
