基于元路径与层次注意力的时序异质信息网络表示学习方法

异质信息网络表示学习在节点分类、链接预测、个性化推荐等多个领域上被广泛应用.现有的异质信息网络表示学习方法大多集中在静态网络,忽略网络中时间属性对节点表示的影响.为了解决该问题,文中提出基于元路径和层次注意力的时序异质信息网络表示学习方法.利用元路径捕获异质信息网络中的结构和语义信息.通过时间衰减注意力层,捕获不同元路径实例在特定时间对目标节点的影响.通过元路径级别注意力,融合不同元路径下的节点表示,得到最终表示.在DBLP、IMDB数据集上的实验表明,文中方法在节点分类和节点聚类任务上均可达到较优效果.

Meta-Path and Hierarchical Attention Based Temporal Heterogeneous Information Network Representation Learning

Heterogeneous information network representation learning is widely applied in many fields including node classification, link prediction and personalized recommendation. The existing heterogeneous information network representation learning methods mainly focus on static networks but ignore the influence of time on node representations. To address this problem, a meta-path and hierarchical attention based temporal heterogeneous network representation learning method is proposed. The meta-paths are utilized to capture the structural and semantic information in heterogeneous information networks. Through the time decay attention layer, the impact of different meta-path instances at a specific time on the target node is captured. Through the meta-path level attention, the node representation under different meta-paths is fused to obtain the final representation. The experiments on DBLP and IMDB datasets show that the proposed method achieves better results on the tasks of node classification and node clustering.