  Influence maximization has found applications in a wide range of real-world
problems, for instance, viral marketing of products in an online social
network, and information propagation of valuable information such as job
vacancy advertisements and health-related information. While existing
algorithmic techniques usually aim at maximizing the total number of people
influenced, the population often comprises several socially salient groups,
e.g., based on gender or race. As a result, these techniques could lead to
disparity across different groups in receiving important information.
Furthermore, in many of these applications, the spread of influence is
time-critical, i.e., it is only beneficial to be influenced before a time
deadline. As we show in this paper, the time-criticality of the information
could further exacerbate the disparity of influence across groups. This
disparity, introduced by algorithms aimed at maximizing total influence, could
have far-reaching consequences, impacting people's prosperity and putting
minority groups at a big disadvantage. In this work, we propose a notion of
group fairness in time-critical influence maximization. We introduce surrogate
objective functions to solve the influence maximization problem under fairness
considerations. By exploiting the submodularity structure of our objectives, we
provide computationally efficient algorithms with guarantees that are effective
in enforcing fairness during the propagation process. We demonstrate the
effectiveness of our approach through synthetic and real-world experiments.
