A Faster, More Efficient Approach to Large-Scale Graph Computation

WormHole𝑀 significantly outperforms MLL in handling complex graph computations. While MLL often fails to complete setup on large graphs, WormHole𝑀 runs efficiently on the core, offering orders-of-magnitude improvements in both time and memory usage. Although its setup cost is higher than WormHole𝐻, its inquiry speed is substantially faster, proving it a powerful primitive for scalable graph learning. Future work will explore hybrid models combining WormHole with traditional methods for even better performance.