The Marginal Benefit of Monitor Placement on Networks

Inferring the structure of an unknown network is a difficult problem of interest to researchers, academics , and industrialists . We develop a novel algorithm to infer nodes and edges in an unknown network. Our algorithm utilizes monitors that detect incident edges and adjacent nodes with their labels and degrees. The algorithm infers the network through a preferential random walk with a probabilistic restart at a previously discovered but unmonitored node, or a random teleportation to an unexplored node. Our algorithm outperforms random walk inference and random placement of monitors inference in edge discovery in all test cases. Our algorithm outperforms both methodologies in node inference in synthetic test networks; on real networks it outperforms them in the beginning of the inference. Finally, a website was created where these algorithms can be tested live on preloaded networks or custom networks as desired by the user. The visualization also displays the network as it is being inferred, and provides other statistics about the real and inferred networks.