Graph data is crucial for modeling complex relationships in various fields, but conventional graph computing methods struggle to handle increasingly intricate and large-scale graph data. Electric ...
In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep ...
Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...
Decades ago, Paul Erdős used randomness to illuminate the vast and weird world of networks. Now mathematicians are making his ...
In the age when data is everything to a business, managers and analysts alike are looking to emerging forms of databases to paint a clear picture of how data is delivering to their businesses. The ...
Powers cloud applications that need to manage complex and highly connected data Graph-specific use cases include master data management, recommendation & personalization, security & ...
Irradiated esophageal squamous cell carcinoma cells induced the increase of Treg by TGF-beta. Sensitive and dynamic CTCs measurement for prediction and monitoring of PD-1 therapy in GC/EGJC patients: ...
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