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008			230515s2023 si | o |||| 0|eng d
020			_a9789819901852
024	7		_a10.1007/978-981-99-0185-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA166 _b2023 EB
100	1		_aDai, Qionghai _d1964- _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689475
245	1	0	_aHypergraph Computation _cby Qionghai Dai, Yue Gao
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aArtificial Intelligence: Foundations Theory and Algorithms _x2365-306X
505	0		_aChapter 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.
520			_aThis 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9146336 _aGrafos, Teoría de
700	1		_9670244 _aGao, Yue _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-0185-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI