Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property. (arXiv:2210.05947v1 [cs.IR])

In the age of big data, the demand for hidden information mining in
technological intellectual property is increasing in discrete countries.
Definitely, a considerable number of graph learning algorithms for
technological intellectual property have been proposed. The goal is to model
the technological intellectual property entities and their relationships
through the graph structure and use the neural network algorithm to extract the
hidden structure information in the graph. However, most of the existing graph
learning algorithms merely focus on the …

arxiv convolution hypergraph intellectual property representation representation learning