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Found 4 papers, 2 papers with code
PathMLP: Smooth Path Towards High-order Homophily
However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency.
Clarify Confused Nodes via Separated Learning
A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics and low efficiency.
Neighborhood Homophily-based Graph Convolutional Network
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks.
Data Augmentation on Graphs: A Technical Survey
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems.
