Matrix computations (3rd ed.)
Sparse graphical models for exploring gene expression data
Journal of Multivariate Analysis
Networking Wireless Sensors
Complex Graphs and Networks (Cbms Regional Conference Series in Mathematics)
Complex Graphs and Networks (Cbms Regional Conference Series in Mathematics)
Decoding by linear programming
IEEE Transactions on Information Theory
Stable Recovery of Sparse Signals Via Regularized Minimization
IEEE Transactions on Information Theory
Sparse matrix inversion with scaled Lasso
The Journal of Machine Learning Research
Hi-index | 754.84 |
A method of network filtering has been proposed recently to detect the effects of certain external perturbations on the interacting members in a network. However, with large networks, the goal of detection seems a priori difficult to achieve, especially since the number of observations available often is much smaller than the number of variables describing the effects of the underlying network. Under the assumption that the network possesses a certain sparsity property, we provide a formal characterization of the accuracy with which the external effects can be detected, using a network filtering system that combines Lasso regression in a sparse simultaneous equation model with simple residual analysis. We explore the implications of the technical conditions underlying our characterization, in the context of various network topologies, and we illustrate our method using simulated data.