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

我们取得了四大成果。A)我们介绍了几种关键的GNN设计策略，并提出了新的模型。B)提出了一种用于异构图嵌入的单层聚集方案，解决了现有两层方案存在的降权问题。C)针对不平衡图结点分类问题，提出了数据生成策略和新的GNN模型。D)我们使用图学习方法来预测直播平台上的捐款。

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.