Learning on Partial-Order Hypergraphs
Fuli Feng, Xiangnan He, Yiqun Liu, Liqiang Nie, Tat‐Seng Chua · 2018
Graph-based learning methods explicitly consider the relations between two entities (i.e., vertices) for learning the prediction function. They have been widely used in semi-supervised learning, manifold ranking, and clustering, among other tasks. Enhancing the expressiveness of simple graphs, hypergraphs formulate an edge as a link to multiple vertices, so as to model the higher-order relations among entities. For example, hyperedges in a hypergraph can be used to encode the similarity among vertices.