ResGAT: an improved graph neural network based on multi-head attention mechanism and residual network for paper classification

Paper classification plays a pivotal role in facilitating precise literature retrieval, recommendations, and bibliometric analyses. However, current text-based methods predominantly emphasize intrinsic features such as titles, abstracts, and keywords, overlooking the valuable insights concealed within reference papers (i.e., cited papers). As a result, this oversight leads to reduced classification accuracy. In contrast, as a practical deep learning approach, graph neural networks incorporate the characteristics of reference papers to enhance paper classification. Nevertheless, traditional graph neural networks encounter limitations when handling intricate multi-level citation relationships in academic papers. To address these challenges, we introduce an enhanced graph neural network model for academic paper classification. This model integrates a multi-head attention mechanism and a residual network structure to dynamically allocate weights to various nodes within the graph, thereby enhancing its ability to handle complex multi-level citation relationships. Our experimental findings on an extensive real-world dataset demonstrate that our model achieves an accuracy of 61%, surpassing traditional graph neural networks by over 4%. Additionally, we have made the relevant datasets and models accessible on our GitHub repository. (https://github.com/xuejianhuang/ResGAT-for-paper-classification).