  We perform sensitivity analyses to assess the impact of missing data on the
structural properties of social networks. The social network is conceived of as
being generated by a bipartite graph, in which actors are linked together via
multiple interaction contexts or affiliations. We discuss three principal
missing data mechanisms: network boundary specification (non-inclusion of
actors or affiliations), survey non-response, and censoring by vertex degree
(fixed choice design), examining their impact on the scientific collaboration
network from the Los Alamos E-print Archive as well as random bipartite graphs.
The results show that network boundary specification and fixed choice designs
can dramatically alter estimates of network-level statistics. The observed
clustering and assortativity coefficients are overestimated via omission of
interaction contexts (affiliations) or fixed choice of affiliations, and
underestimated via actor non-response, which results in inflated measurement
error. We also find that social networks with multiple interaction contexts
have certain surprising properties due to the presence of overlapping cliques.
In particular, assortativity by degree does not necessarily improve network
robustness to random omission of nodes as predicted by current theory.
