基于改进型图神经网络的学术论文分类模型

Classification Model for Scholarly Articles Based on Improved Graph Neural Network

【目的】解决传统图神经网络的过平滑问题,实现图神经网络不同深度和不同邻居的权重自适应分配,提高学术论文分类的性能。【方法】提出一种基于多头注意力机制和残差网络结构的改进型图神经网络学术论文分类模型。首先,基于多头注意力机制学习文献间多种关联特征,实现不同邻居节点权重的自适应分配;然后,基于残差网络结构聚合模型每层节点的输出,为模型提供自适应性聚合半径的学习机制;最后,基于改进型图神经网络学习论文引用关系图中每个节点的特征表示,将该特征输入多层全连接网络中得到最终分类结果。【结果】在大规模真实数据集上的实验结果表明,该模型准确率达到0.61,比图卷积神经网络和Transformer模型的准确率分别高出0.04和0.14。【局限】对小类别样本和难于区分的样本分类准确率不高。【结论】改进的图神经网络能够有效避免过平滑问题,实现不同权重的自适应分配。

[Objective]This paper tries to address the over-smoothing issues of the traditional graph neural network,and then realizes the weight adaptive allocation of different depths and neighbors,aiming to improve the performance of academic literature classification.[Methods]We proposed an improved graph neural network model for academic paper classification.First,with the help of multi-head attention mechanism,the new model learned a variety of related features among documents,and adaptively distributing the weights of different neighbor nodes.Then,based on the residual network structure,the model aggregated outputs of each layer node,and provided the learning of adaptive aggregation radius.Finally,with the help of improved graph neural network,the model learned feature representation of each node in the paper citation graph,which was input into the multilayer fully connected network to obtain the final classification.[Results]We examined our model on large-scale real datasets.The accuracy of our model reached 0.61,which is 0.04 and 0.14 higher than those of the GCN and Transformer models.[Limitations]More research is needed to improve the classification accuracy of small categories and difficult to distinguish samples.[Conclusions]The improved graph neural network can effectively conduct classification for academic articles.