1 citations · 1 across the 4 of their papers we have counts for
4 papers
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Tai Hasegawa, Sukwon Yun, Xin Liu +2
Graph Neural Networks (GNNs) have achieved notable success in various applications over graph data. However, recent research has revealed that real-world graphs often contain noise…
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Hejie Cui, Xinyu Fang, Zihan Zhang +7
Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., su…
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Xin Liu, Xunbin Xiong, Mingyu Yan +4
Large-scale graphs are ubiquitous in real-world scenarios and can be trained by Graph Neural Networks (GNNs) to generate representation for downstream tasks. Given the abundant inf…
Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching
Xin Liu, Yangqiu Song
Graph neural networks (GNNs) and message passing neural networks (MPNNs) have been proven to be expressive for subgraph structures in many applications. Some applications in hetero…