6 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
A Cross-graph Tuning-free GNN Prompting Framework
Yaqi Chen, Shixun Huang, Ryan Twemlow +6
GNN prompting aims to adapt models across tasks and graphs without requiring extensive retraining. However, most existing graph prompt methods still require task-specific parameter…
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
Taiqiang Wu, Zhe Zhao, Jiahao Wang +4
Distilling high-accuracy Graph Neural Networks (GNNs) to low-latency multilayer perceptions (MLPs) on graph tasks has become a hot research topic. However, conventional MLP learnin…
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification
Xiaohe Li, Lijie Wen, Yawen Deng +4
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of…
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Langzhang Liang, Cuiyun Gao, Shiyi Chen +5
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, l…