Hypergraph computation / Qionghai Dai, Yue Gao.
Dai, Qionghai, 1964- author.; Gao, Yue (Researcher), author.
2023
QA166.23
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Title Hypergraph computation / Qionghai Dai, Yue Gao.
Author Dai, Qionghai, 1964- author.
ISBN 9789819901852 (electronic bk.)
9819901855 (electronic bk.)
9789819901845
DOI https://doi.org/10.1007/978-981-99-0185-2
Published Singapore : Springer, [2023]
Copyright ©2023
Language English
Description 1 online resource (xvi, 244 pages).
Item Number 10.1007/978-981-99-0185-2 doi
Call Number QA166.23
Dewey Decimal Classification 511/.5
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Open access
Source of Description Online resource; title from PDF title page (SpringerLink, viewed May 24, 2023).
Added Author Gao, Yue (Researcher), author.
Series Artificial intelligence: foundations, theory, and algorithms. 2365-306X
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Chapter 1. Introduction
Chapter 2. Mathematical Foundations of Hypergraph
Chapter 3. Hypergraph Computation Paradigms
4. Hypergraph Modeling
Chapter 5. Typical Hypergraph Computation Tasks
6. Hypergraph Structure Evolution
Chapter 7. Neural Networks on Hypergraph
Chapter 8. Large Scale Hypergraph Computation
Chapter 9. Hypergraph Computation for Social Media Analysis
Chapter 10. Hypergraph Computation for Medical and Biological Applications
Chapter 11. Hypergraph Computation for Computer Vision
Chapter 12.The Deep Hypergraph Library
Chapter 13. Conclusions and Future Work.

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