Researchers have developed a new method for estimating the average distance between nodes in large-scale networks. They tested two approaches: graph sampling and landmark-based methods. The latter, which uses probabilistic data structures like HyperLogLog, outperformed the former and provided accurate results with minimal memory usage. In particular, the Eppstein-Wang algorithm achieved high accuracy and low computation time, making it a practical solution for large graphs. --- Why it matters: This matters to AI researchers because estimating average distances is crucial in graph analytics, which has applications in network optimization, traffic flow modeling, and social network analysis. The new method provides a scalable solution that can handle large graphs with minimal memory usage. Source: https://arxiv.org/abs/2608.16916