基于跨视图原型非对比学习的异构图嵌入模型.

Heterogeneous graph embedding models based on non-contrastive learning (NCL) do not rely on negative sampling to learn the intrinsic features and patterns, which may cause the model fail to efficiently learn the differences between vertexes. This paper proposed a heterogeneous graph embedding model based on cross-view prototype non-contrastive learning (XPNCL), which learnt better node representations for downstream tasks by finding additional positive samples with more contextual information, and reconsidered the similarity between positive samples. The model firstly designed a tree structure based on random walks in heterogeneous graph. This directed filtering tree (DFT) about positive samples contained rich neighboring and semantic information by filtering out random walk paths that satisfied local structural constraints. Secondly, to achieve the alignment of similar samples in terms of numerical and quantitative from multiple dimensions, XP-NCL defined the cross-view prototype index (ISDR) and peak operator based on the characteristics of heterogeneous graphs. Furthermore, the model trained using stop-gradient updating. Finally, experiments verify the classification and clustering performance of the node on ACM, DBLP and freebase datasets, and the results show that even without the negative samples, the XP-NCL representation can achieve superior performance in many cases compared to other homogeneous and heterogeneous graph baselines. [ABSTRACT FROM AUTHOR]