Pod A Conference Room
Micro Working Group

All day


Our campus is closed to the public for this event.

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.


Anshuman SwainAnshuman SwainHarvard Society Fellow and JSMF Postdoctoral Fellow Harvard University
Mari KawakatsuMari KawakatsuJSMF Postdoctoral Fellow University of Pennsylvania
Harrison HartleHarrison HartleComplexity Postdoctoral Fellow Santa Fe Institute
Grace Smith-VidaurreGrace Smith-VidaurreAssistant Professor Michigan State University

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