Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which enable the processing of graph-structured data without relying on predefined graph structures, are gaining importance in an increasingly wide variety of applications. As these networks demonstrate proficiency across a range of tasks, they become lucrative targets for model-stealing attacks where an adversary seeks to replicate the functionality of the targeted network. A large effort has been made to develop model-stealing attacks that focus on models trained with images and texts. However, little attention has been paid to GNNs trained on graph data. This paper introduces a novel method for unsupervised model-stealing attacks against inductive GNNs, based on graph contrasting learning and spectral graph augmentations to efficiently extract information from the target model. The proposed attack is thoroughly evaluated on six datasets. The results show that this approach demonstrates a higher level of efficiency compared to existing stealing attacks. More concretely, our attack outperforms the baseline on all benchmarks achieving higher fidelity and downstream accuracy of the stolen model while requiring fewer queries sent to the target model.

How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing? Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland; aprego@ippt.pan.pl Abstract--The impact of Artificial Intelligence (AI) is expanding rapidly, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously needs novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by delivering tools for atomic-precision surface mapping. Besides many advantages, it also has some drawbacks, eg. In this paper, we focus on the potential possibilities for supporting SPM-based measurements, putting emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms as well as quantum computing (QC). It turned out that AI can be helpful in the experimental processes automation in routine operations, the algorithmic search for good sample regions, and shed light on the structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AIbased algorithms and QC may have a huge potential to increase the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for the improvement of AI-QC-powered SPM. I. INTRODUCTION scanning near field optical microscopy (SNOM) are universal tools for materials' surface characterization. SPM enables to obtain a high-resolution 3D surface profile in a nondestructive measurement.