This open access book discusses the theory and methods of hypergraph computation. Many
underlying relationships among data can be represented using graphs for example in the areas
including computer vision molecular chemistry molecular biology etc. In the last decade
methods like graph-based learning and neural network methods have been developed to process
such data they are particularly suitable for handling relational learning tasks. In many
real-world problems however relationships among the objects of our interest are more complex
than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably
lead to loss of information which can be expected valuable for learning tasks. Hypergraph as a
generation of graph has shown superior performance on modelling complex correlations compared
with graph. Recent years have witnessed a great popularity of researches on hypergraph-related
AI methods which have been used in computer vision social media analysis etc. We summarize
these attempts as a new computing paradigm called hypergraph computation which is to
formulate the high-order correlations underneath the data using hypergraph and then conduct
semantic computing on the hypergraph for different applications. The content of this book
consists of hypergraph computation paradigms hypergraph modelling hypergraph structure
evolution hypergraph neural networks and applications of hypergraph computation in different
fields. We further summarize recent achievements and future directions on hypergraph
computation in this book.
