Neural Network based Graph Learning: Model Evolution and Real-World Application

我们取得了四项主要成果。a）我们介绍了GNN的几个关键设计策略，并提出了新的模型。B）我们提出了一种用于异构图嵌入的单层聚合方案，该方案解决了与当前双层方案相关联的降权问题。c）提出了解决不平衡图节点分类问题的数据生成策略和新的GNN模型。d）我们采用图学习方法来预测直播平台上的捐赠。

We have achieved four main outcomes. a) We introduced several key design strategies for GNNs and proposed new models. b) We proposed single-level aggregation scheme for heterogeneous graph embedding, which addresses the down-weighting issue associated with the current bi-level scheme. c) We proposed data generation strategy and new GNN models for tackling the imbalanced graph node classification problem. d) We employed graph learning approach for predicting the donations on live streaming platforms.