Hypergraph Computation / by Qionghai Dai, Yue Gao.
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 le...
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Superior document: | Artificial Intelligence: Foundations, Theory, and Algorithms, |
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Place / Publishing House: | Singapore : : Springer Nature Singapore :, Imprint: Springer,, 2023. |
Year of Publication: | 2023 |
Edition: | 1st ed. 2023. |
Language: | English |
Series: | Artificial intelligence (Berlin, Germany)
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Physical Description: | 1 online resource (xv, 244 pages) :; illustrations |
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Dai, Qionghai, author. Hypergraph Computation [electronic resource] / by Qionghai Dai, Yue Gao. 1st ed. 2023. Singapore : Springer Nature Singapore : Imprint: Springer, 2023. 1 online resource (xv, 244 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Artificial Intelligence: Foundations, Theory, and Algorithms, 2365-306X Includes bibliographical refences 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. 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. Open Access Artificial intelligence. Machine learning. Artificial intelligence—Data processing. Artificial Intelligence. Machine Learning. Data Science. 981-9901-84-7 Gao, Yue, author. Artificial intelligence (Berlin, Germany) |
language |
English |
format |
Electronic eBook |
author |
Dai, Qionghai, Gao, Yue, |
spellingShingle |
Dai, Qionghai, Gao, Yue, Hypergraph Computation Artificial Intelligence: Foundations, Theory, and Algorithms, 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. |
author_facet |
Dai, Qionghai, Gao, Yue, Gao, Yue, |
author_variant |
q d qd y g yg |
author_role |
VerfasserIn VerfasserIn |
author2 |
Gao, Yue, |
author2_role |
TeilnehmendeR |
author_sort |
Dai, Qionghai, |
title |
Hypergraph Computation |
title_full |
Hypergraph Computation [electronic resource] / by Qionghai Dai, Yue Gao. |
title_fullStr |
Hypergraph Computation [electronic resource] / by Qionghai Dai, Yue Gao. |
title_full_unstemmed |
Hypergraph Computation [electronic resource] / by Qionghai Dai, Yue Gao. |
title_auth |
Hypergraph Computation |
title_new |
Hypergraph Computation |
title_sort |
hypergraph computation |
series |
Artificial Intelligence: Foundations, Theory, and Algorithms, |
series2 |
Artificial Intelligence: Foundations, Theory, and Algorithms, |
publisher |
Springer Nature Singapore : Imprint: Springer, |
publishDate |
2023 |
physical |
1 online resource (xv, 244 pages) : illustrations |
edition |
1st ed. 2023. |
contents |
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. |
isbn |
9789819901845 9789819901852 981-9901-85-5 981-9901-84-7 |
issn |
2365-306X |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q334-342 |
callnumber-sort |
Q 3334 3342 |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
006 - Special computer methods |
dewey-full |
006.3 |
dewey-sort |
16.3 |
dewey-raw |
006.3 |
dewey-search |
006.3 |
oclc_num |
1380015458 |
work_keys_str_mv |
AT daiqionghai hypergraphcomputation AT gaoyue hypergraphcomputation |
status_str |
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ids_txt_mv |
(CKB)5580000000542371 (MiAaPQ)EBC30544995 (Au-PeEL)EBL30544995 (DE-He213)978-981-99-0185-2 (PPN)270615695 (OCoLC)1380015458 (EXLCZ)995580000000542371 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Artificial Intelligence: Foundations, Theory, and Algorithms, |
is_hierarchy_title |
Hypergraph Computation |
container_title |
Artificial Intelligence: Foundations, Theory, and Algorithms, |
author2_original_writing_str_mv |
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