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Poster
Learning the Structure of Large Networked Systems Obeying Conservation Laws
Anirudh Rayas · Rajasekhar Anguluri · Gautam Dasarathy
Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws. Examples of this phenomenon include the Kirchoff laws in electric networks and opinion consensus in social networks. Conservation laws in networked systems are modeled as balance equations of the form $X = B^\ast Y$, where the sparsity pattern of $B^\ast \in \mathbb{R}^{p\times p}$ captures the connectivity of the network on $p$ nodes, and $Y, X \in \mathbb{R}^p$ are vectors of ''potentials'' and ''injected flows'' at the nodes respectively. The node potentials $Y$ cause flows across edges which aim to balance out the potential difference, and the flows $X$ injected at the nodes are extraneous to the network dynamics. In several practical systems, the network structure is often unknown and needs to be estimated from data to facilitate modeling, management, and control. To this end, one has access to samples of the node potentials $Y$, but only the statistics of the node injections $X$. Motivated by this important problem, we study the estimation of the sparsity structure of the matrix $B^\ast$ from $n$ samples of $Y$ under the assumption that the node injections $X$ follow a Gaussian distribution with a known covariance $\Sigma_X$. We propose a new $\ell_{1}$regularized maximum likelihood estimator for tackling this problem in the highdimensional regime where the size of the network may be vastly larger than the number of samples $n$. We show that this optimization problem is convex in the objective and admits a unique solution. Under a new mutual incoherence condition, we establish sufficient conditions on the triple $(n,p,d)$ for which exact sparsity recovery of $B^\ast$ is possible with high probability; $d$ is the degree of the underlying graph. We also establish guarantees for the recovery of $B^\ast$ in the elementwise maximum, Frobenius, and operator norms. Finally, we complement these theoretical results with experimental validation of the performance of the proposed estimator on synthetic and realworld data.
Author Information
Anirudh Rayas (Arizona State University)
Rajasekhar Anguluri (Arizona State University)
I am a postdoc at Arizona State University, working with Lalitha Sankar, Gautam Dasarathy, and Oliver Kosut. I received my M.S. and Ph.D. in Statistics and Mechanical Engineering in 2019, respectively, from the University of California  Riverside, where I was advised by Fabio Pasqualetti. My research interests are high dimensional statistics, statistical signal processing, control theory, and power systems.
Gautam Dasarathy (Arizona State University)
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