Data Pricing for Graph Neural Networks without Pre-purchased Inspection
This addresses a practical issue in model marketplaces for data owners and consumers, offering a novel solution to data pricing without pre-purchased inspection, though it is incremental in improving existing trading mechanisms.
The paper tackles the problem of incentivizing data owners to contribute data for training graph neural networks without requiring them to share data before payment, by proposing a mechanism that assesses data importance based on structural importance and compensates accordingly, achieving up to 40% improvement in MacroF1 and MicroF1 metrics over baselines.
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.