May 13, 2024, 4:11 a.m. | Yuxiang Zhang, Xin Liu, Meng Wu, Wei Yan, Mingyu Yan, Xiaochun Ye, Dongrui Fan

cs.CR updates on

arXiv:2405.06247v1 Announce Type: cross
Abstract: Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to address the challenges of ever-growing real-world graphs. However, current adversarial attack methods on GNNs neglect the characteristics and applications of the distributed scenario, leading to suboptimal performance and inefficiency in attacking distributed GNN training.
In this study, we introduce Disttack, the first framework of adversarial attacks for distributed GNN …

address adversarial adversarial attack adversarial attacks applications arxiv attack attacks challenges computing cs.lg current distributed graph graphs networks neural networks nodes process real solution training world

Sr. Product Manager

@ MixMode | Remote, US

Information Security Engineers

@ D. E. Shaw Research | New York City

Technology Security Analyst

@ Halton Region | Oakville, Ontario, Canada

Senior Cyber Security Analyst

@ Valley Water | San Jose, CA

Information Security (Network) Consultant

@ Xcellink Pte Ltd | Singapore, Singapore, Singapore

Information Security Management System Manager

@ Babcock | Bristol, GB, BS3 2HQ