论文标题
基于图形神经网络的科学纸质分类，具有超图自我发项机制
Scientific Paper Classification Based on Graph Neural Network with Hypergraph Self-attention Mechanism
论文作者
论文摘要
近年来，科学论文的数量迅速增加。如何充分利用科学论文进行研究非常重要。通过对科学论文的高质量分类，研究人员可以迅速从大量的科学资源中找到所需的资源内容。科学论文的分类将有效地帮助研究人员过滤冗余信息，快速准确地获得搜索结果，并提高搜索质量，这对于科学资源管理是必要的。本文提出了一种基于HyperGraph神经网络（SPHNN）的科学技术分类方法。在科学论文的异质信息网络中，重复的高阶子图被建模为由多个相关节点组成的超蛋白。然后将整个异质信息网络转化为由不同的超中期组成的超图。图形卷积操作是在HyperGraph结构上进行的，Hyperedges自我发项机制被引入到HyperGraph中的不同类型的节点，以便最终的节点表示可以有效地维持高阶最近的邻居关系和复杂的语义信息。最后，通过与其他方法进行比较，我们证明了本文提出的模型改善了其性能。
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.
