EasyHypergraph • Revolutionizing Higher-Order Network Analysis with Unmatched Speed and Efficiency. A groundbreaking open-source software transforms higher-order network analysis by addressing the limitations of traditional pairwise network models. Unlike standard graphs, hypergraphs use hyperedges to connect multiple nodes, capturing complex multi-entity interactions in fields like social networks, biology, and scientometrics. Developed by Ye et al., EasyHypergraph integrates hypergraph analysis and learning into a single, efficient framework. It outperforms existing tools like HNX and XGI, achieving speedups exceeding 11,000 times for key computations and significantly reducing memory usage, enabling large-scale analyses on standard hardware. Its applications span political science, uncovering legislative dynamics, and scientometrics, revealing co-citation patterns. By supporting hypergraph neural networks with ~70% faster training, EasyHypergraph democratizes complex network research, fostering innovation through its accessible, extensible design. This tool overcomes computational bottlenecks and empowers researchers to explore intricate systems, heralding a new era in network science.