Jianxiang Yu

Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes

2023 · arXiv (Cornell University)

Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existing heterogeneous graph neural networks (HGNNs) ignore the problems of missing attributes, …

RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning

2024 · arXiv (Cornell University)

The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP). Initial graph prompt tuning approaches tailored specialized …