Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity
This addresses the challenge of identifying key individuals in group interactions for applications like social network analysis, but it is incremental as it builds on semi-supervised learning for a specific task.
The paper tackles the problem of identifying group anchors, significant members in real-world group interactions like co-authorship, under label scarcity, and introduces AnchorRadar, a semi-supervised method that achieves higher accuracy with 10.2× less training time and 43.6× fewer parameters than baselines on average.
Group interactions occur in various real-world contexts, e.g., co-authorship, email communication, and online Q&A. In each group, there is often a particularly significant member, around whom the group is formed. Examples include the first or last author of a paper, the sender of an email, and the questioner in a Q&A session. In this work, we discuss the existence of such individuals in real-world group interactions. We call such individuals group anchors and study the problem of identifying them. First, we introduce the concept of group anchors and the identification problem. Then, we discuss our observations on group anchors in real-world group interactions. Based on our observations, we develop AnchorRadar, a fast and effective method for group anchor identification under realistic settings with label scarcity, i.e., when only a few groups have known anchors. AnchorRadar is a semi-supervised method using information from groups both with and without known group anchors. Finally, through extensive experiments on thirteen real-world datasets, we demonstrate the empirical superiority of AnchorRadar over various baselines w.r.t. accuracy and efficiency. In most cases, AnchorRadar achieves higher accuracy in group anchor identification than all the baselines, while using 10.2$\times$ less training time than the fastest baseline and 43.6$\times$ fewer learnable parameters than the most lightweight baseline on average.