Jiajun Bu

Learning Temporal Interaction Graph Embedding via Coupled Memory Networks

2020

Graph embedding has become the research focus in both academic and industrial communities due to its powerful capabilities. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, …

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

2020 · arXiv (Cornell University)

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification …

Distilling Holistic Knowledge with Graph Neural Networks

2021 · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network. Existing KD methods have mainly considered two types of knowledge, namely the individual knowledge and the …

Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications

2020

The e-commerce appeals to a multitude of online shoppers by providing personalized experiences and becomes indispensable in our daily life. Accurately predicting user preference and making a recommendation of favorable items plays a crucial role …