Most real networks are too large or they are not available for real time analysis. Therefore, in practice, decisions are made based on partial information about the ground truth network. It is of great interest to have metrics to determine if an inferred network (the partial information network) is similar to the ground truth. In this paper we develop a test for similarity between the inferred and the true network. Our research utilizes a network visualization tool, which systematically discovers a network, producing a sequence of snapshots of the network. We introduce and test our metric on the consecutive snapshots of a network, and against the ground truth. To test the scalability of our metric we use a random matrix theory approach while discovering Erdös-Rényi graphs. This scaling analysis allows us to make predictions about the performance of the discovery process.

Air transportation networks develop independently based on the needs of carriers, economic and political factors, as well as their interactions. A deeper study of these networks can provide insights into the growing process and the characteristics of this multi-billion dollar industry. In this regard, reliable network-formation models are required. Generating synthetic models for airline transportation networks is a difficult and current endeavor. The subnetworks induced by airline companies lead naturally to a multilayer structure. Upon a profound analysis of the benchmark European Air Transportation Network (EATN), we propose a novel approach, called Airline Network Generation Emphasizing Layers (ANGEL), to create synthetic multilayer networks mimicking the two patterns typical in air transportation networks, i.e., hub-and-spoke and point-to-point structures. Moreover, we introduce new statistics to analyze the EATN and to validate the synthetic network model, which can be applied to study multilayer networks in general.