Reverse Influential Community Search Over Social Networks (Technical Report)
Qi Wen, Nan Zhang, Yutong Ye, Xiang Lian, Mingsong Chen

TL;DR
This paper introduces the Top-M Reverse Influential Community Search problem, aiming to find seed communities with maximum influence on specific target communities in social networks, using novel pruning and indexing techniques.
Contribution
It proposes new formulations for influence search targeting specific communities, along with efficient algorithms and pruning strategies for these problems.
Findings
Algorithms are effective on real-world social networks.
Proposed methods outperform baseline approaches.
Approaches are scalable under various parameters.
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 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…
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Taxonomy
TopicsWikis in Education and Collaboration · Complex Network Analysis Techniques · Expert finding and Q&A systems
