Data Pricing for Graph Neural Networks without Pre-purchased Inspection

TL;DR

This paper introduces SIMT, a novel mechanism for data trading in graph neural networks that incentivizes data owners without requiring pre-disclosure of data, ensuring fairness and improved model performance.

Contribution

The paper proposes SIMT, a new incentive-compatible, budget-feasible data trading mechanism for GNNs that does not require data owners to share data upfront.

Findings

SIMT outperforms baseline methods by up to 40% in F1 scores.

Theoretical guarantees include incentive compatibility and individual rationality.

Experiments on five datasets validate the effectiveness of SIMT.

Abstract

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.