  The study of influence maximization in social networks has largely ignored
disparate effects these algorithms might have on the individuals contained in
the social network. Individuals may place a high value on receiving
information, e.g. job openings or advertisements for loans. While
well-connected individuals at the center of the network are likely to receive
the information that is being distributed through the network, poorly connected
individuals are systematically less likely to receive the information,
producing a gap in access to the information between individuals. In this work,
we study how best to spread information in a social network while minimizing
this access gap. We propose to use the maximin social welfare function as an
objective function, where we maximize the minimum probability of receiving the
information under an intervention. We prove that in this setting this welfare
function constrains the access gap whereas maximizing the expected number of
nodes reached does not. We also investigate the difficulties of using the
maximin, and present hardness results and analysis for standard greedy
strategies. Finally, we investigate practical ways of optimizing for the
maximin, and give empirical evidence that a simple greedy-based strategy works
well in practice.
