15 citations · 17 across the 10 of their papers we have counts for
4 papers · 1 filter
Robust Graph Fine-Tuning with Adversarial Graph Prompting
Ziyan Zhang, Bo Jiang, Jin Tang
Parameter-Efficient Fine-Tuning (PEFT) method has emerged as a dominant paradigm for adapting pre-trained GNN models to downstream tasks. However, existing PEFT methods usually exh…
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…
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…
Incomplete Graph Representation and Learning via Partial Graph Neural Networks
Bo Jiang, Ziyan Zhang
Graph Neural Networks (GNNs) are gaining increasing attention on graph data learning tasks in recent years. However, in many applications, graph may be coming in an incomplete form…