June 10, 2024, 4:46 a.m. | Venkata Sai Pranav Bachina, Ankit Gangwal, Aaryan Ajay Sharma, Charu Sharma

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04805v1 Announce Type: cross
Abstract: Graph Neural Networks (GNNs) have advanced the field of machine learning by utilizing graph-structured data, which is ubiquitous in the real world. GNNs have applications in various fields, ranging from social network analysis to drug discovery. GNN training is strenuous, requiring significant computational resources and human expertise. It makes a trained GNN an indispensable Intellectual Property (IP) for its owner. Recent studies have shown GNNs to be vulnerable to model-stealing attacks, which raises concerns over …

abstract advanced analysis applications arxiv computational cs.cr cs.lg data discovery drug discovery expertise fields genie gnn gnns graph graph neural networks human link prediction machine machine learning network networks neural networks prediction resources social structured data training type watermarking world

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