As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and ...

Abstract: How can we exploit Label Propagation (LP) to improve the performance of GNN models on heterophilic graphs? Graph Neural Network (GNN) models have received a lot of attention as a powerful ...

Master data science in 2025. Complete guide to machine learning, big data analytics, Python programming, statistical modeling ...

Here, we present stGuide, an attention-based supervised graph learning model designed for cross-slice alignment and efficient label transfer from reference to query datasets. stGuide leverages ...

instances = [ (-4.8447532242074978, -5.6869538132901658), (1.7265577109364076, -2.5446963280374302), (-1.9885982441038819, 1.705719643962865), (-1.999050026772494, -4 ...

We developed multi-label incremental learning code based on the PyCIL toolkit ... can be modified in the corresponding Python file.