Search Results for author:
Found 5 papers, 4 papers with code
NodeMixup: Tackling Under-Reaching for Graph Neural Networks
However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the \emph{under-reaching} issue.
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning
Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions.
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
The two modules can effectively utilize and enhance each other, promoting the model to learn discriminative embeddings.
An Improved Normed-Deformable Convolution for Crowd Counting
However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information.
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs.
