Graph Expansion

Graph expansion, broadly defined, involves strategically growing or modifying graph structures to enhance properties like connectivity, sparsity, or information propagation. Current research focuses on applying graph expansion techniques to diverse areas, including motion planning (using bubble-based algorithms and tree expansion), neural network optimization (through graph rewiring and pruning to maintain performance), and causal discovery (with algorithms like Best Order Score Search). These advancements improve efficiency and accuracy in various applications, from robotics and AI to network analysis and machine learning, by optimizing graph structures for specific tasks.