IEEE

Wasserstein Graph Neural Networks for Graphs With Missing Attributes

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
Time

6 Graphs That Show Where the U.S. Leads China on AI—and Where It Doesn’t

Two important things happened on January 20, 2025. In Washington, D.C., Donald Trump was inaugurated as President of the United States. In Hangzhou, China, a little-known Chinese firm called DeepSeek ...
IEEE

Causal Disentangled Graph Neural Network for Fault Diagnosis of Complex Industrial Process

Abstract: Graph neural networks (GNNs) are good at capturing the intricate topologies and dependencies among components and are outstanding in fault diagnosis tasks of complex industrial process. Bias ...
GitHub

snt-arg/visual_sgraphs

The diagram below shows the detailed architecture of the vS-Graphs framework, highlighting the key threads and their interactions. Modules with a light gray background are inherited directly from the ...