DIVIDE-AND-CONQUER TOMOGRAPHY FOR LARGE-SCALE NETWORKS

This work considers the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. Observations from only a subset of the nodes are collected, and the information is used to infer their local connectivity (local tomography). Recent results establish that, under suitable conditions on the network model, local tomography is achievable with high probability as the network size scales to infinity [1, 2]. Motivated by these results, we explore the possibility of reconstructing a larger network via repeated application of the local tomography algorithm to smaller network portions. A divide-and-conquer strategy is developed and tested numerically on some illustrative examples.