The most active community search in large temporal graphs

作者：

Highlights：

• We first propose a novel (k, t)-active core model to search the most active community in large temporal graphs.

• We proposed two upper-bound based pruning rules to find the (k, t)-core.

• Three carefully designed search strategies are proposed to search the most active community.

摘要

•We first propose a novel (k, t)-active core model to search the most active community in large temporal graphs.•We proposed two upper-bound based pruning rules to find the (k, t)-core.•Three carefully designed search strategies are proposed to search the most active community.

论文关键词：Temporal graph,The most active community search

论文评审过程：Received 3 November 2021, Revised 8 May 2022, Accepted 18 May 2022, Available online 31 May 2022, Version of Record 9 June 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109101