Researchers Identify Vulnerabilities in AirDrop and Quick Share Protocols

A new research paper titled **“Dynamic Graph Neural Networks for Real‑Time Knowledge Base Completion”** was posted on the preprint server arXiv on 27 June 2026. The authors—Dr. Li Wei, Prof. Ananya Gupta, and Dr. Miguel Sánchez—represent the University of California, Stanford University, and the University of São Paulo, respectively. The manuscript, submitted to the Computer Science – Machine Learning category, proposes a novel graph neural network architecture that updates node embeddings incrementally as new facts arrive, aiming to maintain up‑to‑date knowledge base representations without full retraining.

The paper describes a hybrid model that combines a message‑passing scheme with a lightweight attention mechanism to capture both local structure and global context. Experiments on benchmark datasets such as Wikidata and Freebase show a 12 % improvement in link prediction accuracy over state‑of‑the‑art static models, while reducing training