85 citations · 284 across the 33 of their papers we have counts for
6 papers · 1 filter
Fast Adversarial Training with Smooth Convergence
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as c…
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
Xueyan Yin, Feifan Li, Yanming Shen +2
Recently, deep learning methods have made great progress in traffic prediction, but their performance depends on a large amount of historical data. In reality, we may face the data…
Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
Mingqi Yang, Yanming Shen, Heng Qi +1
For learning graph representations, not all detailed structures within a graph are relevant to the given graph tasks. Task-relevant structures can be or which…
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Mingqi Yang, Yanming Shen, Rui Li +3
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and t…
Temporal Knowledge Graph Reasoning Triggered by Memories
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually at…
Breaking the Expressive Bottlenecks of Graph Neural Networks
Mingqi Yang, Yanming Shen, Heng Qi +1
Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs w…