hypergraphlearning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraphlearning has attracted increasing attention due to its flexibility and capability in modeling complex d...
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hypergraphlearning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraphlearning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraphlearning, inductive hypergraphlearning, hypergraph structure updating, and multi-modal hypergraphlearning. After that, we present a tensor-baseddynamichypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraphlearning development toolkit called THU-HyperG.
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