不预付检查数据即可公平定价图神经网络，保障数据提供方利益。
Data Pricing for Graph Neural Networks without Pre-purchased Inspection
- 基于结构重要性评估数据价值，无需提前披露数据
- 在五个数据集上性能提升最高达40%（宏/微F1）
- 适合关注数据隐私与激励机制的模型交易平台
机器学习模型的有效性依赖大量数据。模型市场作为连接模型消费者与数据拥有者的平台，通过模型交易机制激励数据提供者贡献数据并返还高质量模型。然而，现有机制常假设数据方愿意在获得报酬前共享数据，这在现实中不合理。为此，本文提出一种新型机制——结构重要性模型交易（SIMT），通过评估数据的结构重要性，在不泄露数据的前提下采购特征与标签数据，并训练图神经网络。理论上，SIMT保证了激励相容、个体理性和预算可行性。在五个主流数据集上的实验表明，SIMT在宏F1和微F1指标上均较基线方法最高提升40%。
原文摘要 · Abstract (English)
Machine learning (ML) models have become essential tools in various scenarios. Their effectiveness, however, hinges on a substantial volume of data for satisfactory performance. Model marketplaces have thus emerged as crucial platforms bridging model consumers seeking ML solutions and data owners possessing valuable data. These marketplaces leverage model trading mechanisms to properly incentive data owners to contribute their data, and return a well performing ML model to the model consumers. However, existing model trading mechanisms often assume the data owners are willing to share their data before being paid, which is not reasonable in real world. Given that, we propose a novel mechanism, named Structural Importance based Model Trading (SIMT) mechanism, that assesses the data importance and compensates data owners accordingly without disclosing the data. Specifically, SIMT procures feature and label data from data owners according to their structural importance, and then trains a graph neural network for model consumers. Theoretically, SIMT ensures incentive compatible, individual rational and budget feasible. The experiments on five popular datasets validate that SIMT consistently outperforms vanilla baselines by up to $40\%$ in both MacroF1 and MicroF1.
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