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Hypergraph Computation

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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 complexthan 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.

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Detail Information

Series Title

Artificial Intelligence: Foundations, Theory, and Algorithms

Call Number

XVI, 244

Publisher

Springer Singapore : Springer Singapore., 2023

Collation

-

Language

English

ISBN/ISSN

978-981-99-0185-2

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Artificial Intelligence
Structures and Information Theory

Specific Detail Info

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Statement of Responsibility

Qionghai Dai, Yue Gao

Other Information

Cataloger

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Source

https://link.springer.com/book/10.1007/978-981-99-0185-2

Validator

Suwardi

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-981-99-0185-2

Journal Volume

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Journal Issue

-

Subtitle

-

Parallel Title

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