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

TL;DR

This paper introduces a hypergraph neural network with self-attention for classifying scientific papers, enhancing accuracy by modeling high-order relationships in heterogeneous information networks.

Contribution

It proposes a novel hypergraph neural network model with self-attention for scientific paper classification, capturing complex semantic and high-order relationships.

Findings

Improved classification accuracy over existing methods

Effective modeling of high-order subgraphs as hyperedges

Enhanced semantic representation through hyperedge self-attention

Abstract

The number of scientific papers has increased rapidly in recent years. How to make good use of scientific papers for research is very important. Through the high-quality classification of scientific papers, researchers can quickly find the resource content they need from the massive scientific resources. The classification of scientific papers will effectively help researchers filter redundant information, obtain search results quickly and accurately, and improve the search quality, which is necessary for scientific resource management. This paper proposed a science-technique paper classification method based on hypergraph neural network(SPHNN). In the heterogeneous information network of scientific papers, the repeated high-order subgraphs are modeled as hyperedges composed of multiple related nodes. Then the whole heterogeneous information network is transformed into a hypergraph composed of different hyperedges. The graph convolution operation is carried out on the hypergraph structure, and the hyperedges self-attention mechanism is introduced to aggregate different types of nodes in the hypergraph, so that the final node representation can effectively maintain high-order nearest neighbor relationships and complex semantic information. Finally, by comparing with other methods, we proved that the model proposed in this paper has improved its performance.