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oapen-20.500.12657-636102023-06-21T04:26:56Z Hypergraph Computation Dai, Qionghai Gao, Yue Hypergraph Hypergraph Computation Hypergraph Learning Hypergraph Modelling Hypergraph Neural Network Complex Correlation Modelling High-Order Correlation Modelling bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development::UMB Algorithms & data structures 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. 2023-06-20T10:31:03Z 2023-06-20T10:31:03Z 2023 book ONIX_20230620_9789819901852_49 9789819901852 9789819901845 https://library.oapen.org/handle/20.500.12657/63610 eng Artificial Intelligence: Foundations, Theory, and Algorithms application/pdf n/a 978-981-99-0185-2.pdf https://link.springer.com/978-981-99-0185-2 Springer Nature Springer Nature Singapore 10.1007/978-981-99-0185-2 10.1007/978-981-99-0185-2 6c6992af-b843-4f46-859c-f6e9998e40d5 e06840e4-106f-423f-bba7-d034dee9cf25 9789819901852 9789819901845 Springer Nature Singapore 244 Singapore [...] Tsinghua University THU open access
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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 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.
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