  Community search over heterogeneous information networks has been applied to
wide domains, such as activity organization and team formation. From these
scenarios, the members of a group with the same treatment often have different
levels of activity and workloads, which causes unfairness in the treatment
between active members and inactive members (called individual unfairness).
However, existing works do not pay attention to individual fairness and do not
sufficiently consider the rich semantics of HINs (e.g., high-order structure),
which disables complex queries. To fill the gap, we formally define the issue
of individual fairest community search over HINs (denoted as IFCS), which aims
to find a set of vertices from the HIN that own the same type, close
relationships, and small difference of activity level and has been demonstrated
to be NP-hard. To do this, we first develop an exploration-based filter that
reduces the search space of the community effectively. Further, to avoid
repeating computation and prune unfair communities in advance, we propose a
message-based scheme and a lower bound-based scheme. At last, we conduct
extensive experiments on four real-world datasets to demonstrate the
effectiveness and efficiency of our proposed algorithms, which achieve at least
X3 times faster than the baseline solution.
