  The proliferation of social network data has unlocked unprecedented
opportunities for extensive, data-driven exploration of human behavior. The
structural intricacies of social networks offer insights into various
computational social science issues, particularly concerning social influence
and information diffusion. However, modeling large-scale social network data
comes with computational challenges. Though large language models make it
easier than ever to model textual content, any advanced network representation
methods struggle with scalability and efficient deployment to out-of-sample
users. In response, we introduce a novel approach tailored for modeling social
network data in user detection tasks. This innovative method integrates
localized social network interactions with the capabilities of large language
models. Operating under the premise of social network homophily, which posits
that socially connected users share similarities, our approach is designed to
address these challenges. We conduct a thorough evaluation of our method across
seven real-world social network datasets, spanning a diverse range of topics
and detection tasks, showcasing its applicability to advance research in
computational social science.
