SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification

• URL: http://arxiv.org/abs/2508.09544v1
• Date: Wed, 13 Aug 2025 06:58:44 GMT
• Title: SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification
• Authors: Sasan Tavakkol, Lin Chen, Max Springer, Abigail Schantz, Blaž Bratanič, Vincent Cohen-Addad, MohammadHossein Bateni,
• Abstract summary: This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Sampling via Expansion on Graphs) to generate synthetic training data for rare event classification.<n>This synthetic data serve as seeds for semi-supervised label propagation on a similarity graph constructed between the seeds and a large unlabeled dataset.<n> Experiments on the imbalanced SST2 and MHS datasets demonstrate SYNAPSE-G's effectiveness in finding positive labels.
• Score: 18.14381983478547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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