Hypergraph Computation

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This open access book explores the theory and methods of hypergraph computation, highlighting how complex relationships among data can be effectively represented. While traditional graph-based learning and neural network methods have advanced in processing data across fields like computer vision and molecular biology, they often simplify relationships to pairwise interactions, risking valuable information loss. Hypergraphs, as an extension of graphs, excel in modeling these intricate correlations. Recent years have seen a surge in research on hypergraph-related AI methods, applied in areas such as social media analysis and beyond. This book introduces hypergraph computation as a new paradigm for capturing high-order correlations in data, enabling semantic computing for various applications. It covers topics including hypergraph computation paradigms, modeling, structure evolution, neural networks, and applications across diverse fields. Additionally, the book summarizes recent achievements and outlines future directions in hypergraph computation, providing a comprehensive overview of this emerging area of study.