English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The number of scientific papers has increased rapidly in recent years. How tomake good use of scientific papers for research is very important. Through thehigh-quality classification of scientific papers, researchers can quickly findthe resource content they need from the massive scientific resources. Theclassification of scientific papers will effectively help researchers filterredundant information, obtain search results quickly and accurately, andimprove the search quality, which is necessary for scientific resourcemanagement. This paper proposed a science-technique paper classification methodbased on hypergraph neural network(SPHNN). In the heterogeneous informationnetwork of scientific papers, the repeated high-order subgraphs are modeled ashyperedges composed of multiple related nodes. Then the whole heterogeneousinformation network is transformed into a hypergraph composed of differenthyperedges. The graph convolution operation is carried out on the hypergraphstructure, and the hyperedges self-attention mechanism is introduced toaggregate different types of nodes in the hypergraph, so that the final noderepresentation can effectively maintain high-order nearest neighborrelationships and complex semantic information. Finally, by comparing withother methods, we proved that the model proposed in this paper has improved itsperformance.

Scientific Paper Classification Based on Graph Neural Network with Hypergraph Self-attention Mechanism