  The interaction patterns of employees in social and professional networks
play an important role in the success of employees and organizations as a
whole. However, in many fields there is a severe under-representation of
minority groups; moreover, minority individuals may be segregated from the rest
of the network or isolated from one another. While the problem of increasing
the representation of minority groups in various fields has been well-studied,
diver- sification in terms of numbers alone may not be sufficient: social
relationships should also be considered. In this work, we consider the problem
of assigning a set of employment candidates to positions in a social network so
that diversity and overall fitness are maximized, and propose Fair Employee
Assignment (FairEA), a novel algorithm for finding such a matching. The output
from FairEA can be used as a benchmark by organizations wishing to evaluate
their hiring and assignment practices. On real and synthetic networks, we
demonstrate that FairEA does well at finding high-fitness, high-diversity
matchings.
