40 citations · 157 across the 30 of their papers we have counts for
31 papers
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Bo Jiang, Hao Wu, Beibei Wang +2
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct…
Graph Edge Representation via Tensor Product Graph Convolutional Representation
Bo Jiang, Sheng Ge, Ziyan Zhang +3
Graph Convolutional Networks (GCNs) have been widely studied. The core of GCNs is the definition of convolution operators on graphs. However, existing Graph Convolution (GC) operat…
A Unified Graph Selective Prompt Learning for Graph Neural Networks
Bo Jiang, Hao Wu, Ziyan Zhang +2
In recent years, graph prompt learning/tuning has garnered increasing attention in adapting pre-trained models for graph representation learning. As a kind of universal graph promp…
Unifying Graph Contrastive Learning via Graph Message Augmentation
Ziyan Zhang, Bo Jiang, Jin Tang +1
Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that…
AMatFormer: Efficient Feature Matching via Anchor Matching Transformer
Bo Jiang, Shuxian Luo, Xiao Wang +2
Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representatio…
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
Bo Jiang, Fei Xu, Ziyan Zhang +2
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning. However,…