5 citations · 7 across the 5 of their papers we have counts for
6 papers
Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Zhen Zhang, Bingsheng He
Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domai…
PyGDA: A Python Library for Graph Domain Adaptation
Zhen Zhang, Meihan Liu, Bingsheng He
Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their…
Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation
Zhen Zhang, Bingsheng He
Unsupervised graph domain adaptation (UGDA) focuses on transferring knowledge from labeled source graph to unlabeled target graph under domain discrepancies. Most existing UGDA met…
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Qian Wang, Zemin Liu, Zhen Zhang +1
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this chall…
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
Zhen Zhang, Bingqiao Luo, Shengliang Lu +1
Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually imprac…
EX-Graph: A Pioneering Dataset Bridging Ethereum and X
Qian Wang, Zhen Zhang, Zemin Liu +3
While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant…