Decentralized Graph

Decentralized graph research focuses on developing methods for analyzing and processing large, interconnected datasets distributed across multiple, independent entities, addressing privacy and security concerns inherent in centralized approaches. Current research emphasizes developing secure and efficient algorithms, often based on graph neural networks (GNNs) and distributed consensus protocols like Kademlia, to perform tasks such as node classification and link prediction while preserving data privacy through techniques like homomorphic encryption and local differential privacy. This field is significant for enabling collaborative data analysis in sensitive domains like social networks and the Internet of Things, while also fostering the development of a more privacy-respecting and resilient internet infrastructure.