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001467603 001__ 1467603
001467603 003__ OCoLC
001467603 005__ 20230707003330.0
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001467603 007__ cr\cn\nnnunnun
001467603 008__ 230524s2023\\\\si\\\\\\ob\\\\000\0\eng\d
001467603 020__ $$a9789819901852$$q(electronic bk.)
001467603 020__ $$a9819901855$$q(electronic bk.)
001467603 020__ $$z9789819901845
001467603 0247_ $$a10.1007/978-981-99-0185-2$$2doi
001467603 035__ $$aSP(OCoLC)1380015458
001467603 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001467603 049__ $$aISEA
001467603 050_4 $$aQA166.23
001467603 08204 $$a511/.5$$223/eng/20230524
001467603 1001_ $$aDai, Qionghai,$$d1964-$$eauthor.
001467603 24510 $$aHypergraph computation /$$cQionghai Dai, Yue Gao.
001467603 264_1 $$aSingapore :$$bSpringer,$$c[2023]
001467603 264_4 $$c©2023
001467603 300__ $$a1 online resource (xvi, 244 pages).
001467603 336__ $$atext$$btxt$$2rdacontent
001467603 337__ $$acomputer$$bc$$2rdamedia
001467603 338__ $$aonline resource$$bcr$$2rdacarrier
001467603 4901_ $$aArtificial intelligence: foundations, theory, and algorithms,$$x2365-306X
001467603 504__ $$aIncludes bibliographical references.
001467603 5050_ $$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.
001467603 5060_ $$aOpen access$$5GW5XE
001467603 520__ $$aThis open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learning tasks. Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis, etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book.
001467603 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 24, 2023).
001467603 650_0 $$aHypergraphs.
001467603 650_0 $$aArtificial intelligence$$xMathematics.
001467603 655_0 $$aElectronic books.
001467603 7001_ $$aGao, Yue$$c(Researcher),$$eauthor.
001467603 830_0 $$aArtificial intelligence: foundations, theory, and algorithms.$$x2365-306X
001467603 852__ $$bebk
001467603 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-981-99-0185-2$$zOnline Access$$91397441.2
001467603 909CO $$ooai:library.usi.edu:1467603$$pGLOBAL_SET
001467603 980__ $$aBIB
001467603 980__ $$aEBOOK
001467603 982__ $$aEbook
001467603 983__ $$aOnline
001467603 994__ $$a92$$bISE