Finding time-proximity communities in temporal heterogeneous information networks

Abstract

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 core model that captures both structural cohesiveness and temporal relevance. Our model uses a time span constraint to ensure interaction recency and a query interval 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.

Document Type

Conference Proceeding

Date of Publication

1-1-2025

Volume

18

Issue

13

Publication Title

Proceedings of the VLDB Endowment

Publisher

Association for Computing Machinery

School

School of Business and Law

Funders

Australian Research Council

Grant Number

ARC Numbers : DP250100536, DP220102191, DP240101591, DE240100200

Comments

Tang, Y., Liu, C., Chen, L., Zhou, R., & Li, J. (2025). Finding time-proximity communities in temporal heterogeneous information networks. Proceedings of the VLDB Endowment, 18(13), 5740-5752. https://doi.org/10.14778/3773731.3773747

Copyright

subscription content

First Page

5740

Last Page

5752

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Link to publisher version (DOI)

10.14778/3773731.3773747