15 citations · 32 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
LLM-based Human Simulations Have Not Yet Been Reliable
Qian Wang, Jiaying Wu, Zichen Jiang +6
Large Language Models (LLMs) are increasingly employed for simulating human behaviors across diverse domains. However, our position is that current LLM-based human simulations rema…