The text discusses the role of graph analytics in finding hidden relationships within large datasets, with a particular focus on calculating common neighbors between pairs of vertices in a graph. This operation has applications in similarity measurement, link prediction, graph compression, community detection, and more. The author presents an optimized algorithm for counting common neighbors that outperforms the set intersection method and provides a comparative performance analysis of both methods. The implementation of this optimized algorithm is demonstrated using GSQL, a Turing-complete language.