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.

翻译：异质信息网络上的社区搜索已广泛应用于活动组织和团队组建等领域。在这些场景中，接受相同待遇的团队成员往往具有不同的活动水平和工作量，这导致了活跃成员与非活跃成员之间的待遇不公（即个体不公平）。然而，现有研究尚未关注个体公平性，且未能充分挖掘异质信息网络的丰富语义（如高阶结构），无法支持复杂查询。为填补这一空白，我们正式定义了异质信息网络中个体最公平社区搜索（IFCS）问题，旨在从异质信息网络中寻找一组具有相同类型、紧密关系且活动水平差异较小的顶点，并证明该问题为NP难问题。为此，我们首先提出一种基于探索的过滤器，有效缩减了社区的搜索空间。进一步，为避免重复计算并提前剪枝不公平社区，我们分别设计了基于消息的机制和基于下界的机制。最后，我们在四个真实数据集上进行了大量实验，验证了所提算法的有效性和高效性，其运行速度至少比基线解决方案快三倍。