Reverse Influential Community Search Over Social Networks (Technical Report)
CoRR(2024)
Abstract
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 Reverse Influential Community Search (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
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 an RICS
variant, named Relaxed Reverse Influential Community Search (R2ICS), which
returns a subgraph with the 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 RICS and R2ICS approaches on both real-world and synthetic social networks
under various parameter settings.
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