  As an important fundamental task of numerous real-world applications such as
social network analysis and online advertising/marketing, several prior works
studied influential community search, which retrieves a community with high
structural cohesiveness and maximum influences on other users in social
networks. However, previous works usually considered the influences of the
community on arbitrary users in social networks, rather than specific groups
(e.g., customer groups, or senior communities). Inspired by this, we propose a
novel Top-M Reverse Influential Community Search (TopM-RICS) problem, which
obtains a seed community with the maximum influence on a user-specified target
community, satisfying both structural and keyword constraints. To efficiently
tackle the TopM-RICS problem, we design effective pruning strategies to filter
out false alarms of candidate seed communities, and propose an effective index
mechanism to facilitate the community retrieval. We also formulate and tackle a
TopM-RICS variant, named Top-M Relaxed Reverse Influential Community Search}
(TopM-R2ICS), which returns top-M subgraphs with relaxed structural constraints
and having the maximum influence on a user-specified target community.
Comprehensive experiments have been conducted to verify the efficiency and
effectiveness of our TopM-RICS and TopM-R2ICS approaches on both real-world and
synthetic social networks under various parameter settings.
