Collaborative Research: Elements: CHAI: Cyberinfrastructure for Hypergraph-based Analysis and Innovation

Many critical scientific and societal challenges involve interactions among groups of entities that cannot be adequately represented as simple pairwise relationships. For example, chemical reactions, biological signaling pathways, social group dynamics, and epidemic spread involve simultaneous multi-way interactions among more than two entities. Hypergraphs, a mathematical generalization of traditional graphs, provide a more accurate representation by allowing a single edge to connect any number of entities. Despite rapidly growing adoption of hypergraph-based models across biology, health sciences, social networks, and artificial intelligence, researchers lack the scalable, comprehensive, and user-friendly software needed to analyze hypergraphs effectively. This gap forces scientists to rely on simplified graph models that risk losing critical higher-order relationships in their data, potentially leading to incomplete scientific conclusions. This motivates the project - CHAI (Cyberinfrastructure for Hypergraph-based Analysis and Innovation), an open-source parallel software framework that addresses this critical gap. CHAI serves a broad scientific user base through a tiered interface accommodating users ranging from domain scientists who need ready-to-use analytical functions, to intermediate users who wish to tune algorithms, to advanced developers creating entirely new methods. For real-world validation, the CHAI team collaborates with researchers from social network analysis, bioinformatics, food web ecology, additive manufacturing, unmanned aerial vehicles, and cyber-physical systems. The CHAI project aims to develop three foundational technical innovations: (i) a unified data structure that efficiently supports both static and dynamic hypergraph representations on high performance computing platforms including GPUs, (ii) a compact motif-based representation that reduces memory requirements and accelerates hypergraph analysis, and (iii) an extensible parallel algorithm development framework for hypergraphs, enabling researchers to build and contribute new domain-specific algorithms. Together, these innovations enable CHAI to provide scalable and accurate hypergraph analysis. The developed software will be publicly distributed through GitHub, portable software containers, and a graphical drag-and-drop workflow interface that requires no specialized programming expertise, thereby maximizing accessibility across scientific disciplines. CHAI supports the training of graduate students and postdoctoral researchers in parallel algorithm design, high-performance computing, and scientific software development, contributing to building the next generation of computational scientists. Outreach activities extend research opportunities to a broad range of undergraduate and high school students. Community-building through workshops, tutorials, and conference mini-symposia will establish CHAI as the standard cyberinfrastructure platform for hypergraph analysis by enabling advances across a wide range of scientific domains that depend on accurate modeling of complex, multi-way interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.