Yao Cheng

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, …

A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions

2024 · arXiv (Cornell University)

Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar features, have recently attracted significant attention and found many real-world …

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract)

2025

Graphs are ubiquitous in the real world, in graphs, nodes represent entities and edges capture their relationships. Recently, graph neural networks (GNNs) [3]–[6] have been proposed to integrate these two sources of information. In GNNs, …