Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Sampling via Expansion on Graphs), a novel pipeline leveraging Large Language Models (LLMs) to generate synthetic training data for rare event classification, addressing the cold-start problem. This synthetic data serve as seeds for semi-supervised label propagation on a similarity graph constructed between the seeds and a large unlabeled dataset. This identifies candidate positive examples, subsequently labeled by an oracle (human or LLM). The expanded dataset then trains/fine-tunes a classifier. We theoretically analyze how the quality (validity and diversity) of the synthetic data impacts the precision and recall of our method. Experiments on the imbalanced SST2 and MHS datasets demonstrate SYNAPSE-G's effectiveness in finding positive labels, outperforming baselines including nearest neighbor search.
@article{arxiv.2508.09544,
title = {SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification},
author = {Sasan Tavakkol and Lin Chen and Max Springer and Abigail Schantz and Blaž Bratanič and Vincent Cohen-Addad and MohammadHossein Bateni},
journal= {arXiv preprint arXiv:2508.09544},
year = {2025}
}