AffinityTune: A Prompt-Tuning Framework for Few-Shot Anomaly Detection on Graphs

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces a unified task framework grounded in affinity judgment, incorporating multi-granularity tasks (''node vs. adjacent subgraph'' and ''node vs. community'') to learn diverse anomaly priors. % For downstream tasks, we reformulate binary classification into a ''node vs. global representation'' affinity judgment, mapping anomalies to a hypersphere in feature space. % We also design learnable task heads to synergize multiple tasks and propose FlexPrompt, a flexible prompt-tuning strategy for fine-grained adaptation to downstream tasks. % Extensive experiments on real-world and synthetic datasets demonstrate that AffinityTune significantly outperforms existing methods in detection efficacy. % Our contributions include a unified task framework, multi-granularity tasks, and the FlexPrompt strategy, offering a parameter-efficient and adaptable solution for few-shot graph anomaly detection.