Finding Time-Proximity Communities in Temporal Heterogeneous Information Networks

This is the authors' abstract. We don't add key points for this paper.

Community search in heterogeneous information networks (HINs) often neglects temporal dynamics, yielding structures that poorly reflect real-world interactions. We introduce the Temporal HIN Community Search (THCS) problem and propose a novel ( k, T q , P δ )-core model that captures both structural cohesiveness and temporal relevance. Our model uses a time span constraint δ to ensure interaction recency and a query interval T q for flexible temporal exploration, filtering irrelevant connections while preserving structural density. We develop two efficient online algorithms—Center-based Sliding Window search and Incremental Center Expansion—that exploit meta-path symmetry and dynamic connectivity tracking. For frequent queries, we design a Temporal HIN Core Interval-Index (TCI-Index), organising minimal core intervals hierarchically with innovative compression techniques. Experiments on real-world datasets show our methods significantly outperform baselines, finding temporally meaningful communities with high efficiency.