Complete Graph

Complete graphs, representing all possible connections between nodes, are a fundamental structure used across diverse scientific domains. Current research focuses on efficient algorithms for solving optimization problems on complete graphs, such as correlation clustering and worker recruitment in crowdsourcing, often employing graph neural networks (GNNs) and combinatorial approaches. These studies highlight the trade-offs between full-graph and mini-batch training methods for GNNs, with mini-batch approaches demonstrating faster convergence while achieving comparable accuracy. The resulting advancements improve the speed and accuracy of solutions for various applications, including change-point detection in high-dimensional data and efficient document-level event extraction.