Effective Individual Fairest Community Search over Heterogeneous Information Networks
<|reference_start|>Effective Individual Fairest Community Search over Heterogeneous Information Networks: 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.<|reference_end|>
@article{zhao2024effective,
    title={Effective Individual Fairest Community Search over Heterogeneous
  Information Networks},
    author={Taige Zhao, Jianxin Li, Ningning Cui, Wei Luo},
    journal={arXiv preprint arXiv:2404.12107},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12107},
    primaryClass={cs.DB cs.DS cs.SI}
}