Sampling Completeness and Metric Robustness of Bipartite Networks

Data gathering is a messy and complex process, where measuring everything in a system of interest is usually impossible. This leads to incomplete sampling of system observables and when the datasets are used for quantitative analyses, these sampling issues can often lead to spurious results and inferences. Therefore, sampling incompleteness in network datasets is a major problem for quantifying, understanding, and comparing network properties. Furthermore, there is no comprehensive framework to estimate the degree of incompleteness. In this working group, we aim to study, explore, and pose heuristic solutions to this broad theme through a combination of defining novel ways of quantifying completeness and applying them to simulated and empirical bipartite network datasets.