基于元路径属性融合的异质网络表示学习

Heterogeneous network representation learning based on metapath attribute fusion

针对信息网络的表示学习进行研究,提出了一种基于元路径信息融合的异质图神经网络(metapath attribute fusion graph neural network,MAFGNN),通过在异质网络中引入元路径之前将目标节点的邻居信息包括元路径信息融入到节点中,实现目标节点和邻居信息的融合。该方法首先将不同类型的节点属性特征进行维度转换便于后续的融合操作,通过计算目标节点和邻居节点权重值完成目标节点信息的融合操作。然后根据特定元路径对目标节点进行融合,最后在不同元路径间实现不同语义信息的融合操作。在多个异质信息数据集上进行实验表明,MAFGNN模型在处理异质网络节点嵌入方面相比于最先进的基准实验有最好的性能和更加准确的预测结果。

Focusing on the research on representation learning of information networks,a metapath attribute fusion graph neural network(MAFGNN)based on metapath information fusion is proposed,which is to integrate the neighbor information of the target node,including the metapath information,into the node before introducing the metapath in the heterogeneous network to achieve the fusion of target node and neighbor information.This method first converts the attribute features of different types of nodes into dimensions to facilitate subsequent fusion operations.The fusion operation of target node information is completed by calculating the weight values of target nodes and neighbor nodes.Then target nodes are fused according to specific metapaths,and finally different semantic information is fused between different metapaths.Experiments on multiple heterogeneous information datasets show that the MAFGNN model has the best performance and more accurate prediction results than the most advanced benchmark experiments in dealing with heterogeneous network node embedding.