Expander Graph Sampling

Definition ∞ Expander Graph Sampling is a technique used in theoretical computer science and cryptography for efficiently selecting a representative subset of nodes or edges from a larger graph. This method leverages the properties of expander graphs, which are highly connected and sparse, to ensure that the sample accurately reflects the overall structure. In the context of decentralized systems, it can aid in creating efficient and robust communication or verification protocols. This approach aims to reduce computational overhead while maintaining statistical integrity.
Context ∞ While not a mainstream term in daily crypto news, Expander Graph Sampling finds application in advanced cryptographic research relevant to blockchain scalability and privacy solutions. It might be discussed in academic papers or technical deep dives related to zero-knowledge proofs or sharding architectures. Its significance lies in potentially improving the efficiency of data verification and network communication within large distributed ledgers. Future developments in this area could contribute to more performant and secure blockchain designs.