15 citations · 32 across the 11 of their papers we have counts for
12 papers
Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning
Qian Wang, Xuandong Zhao, Zirui Zhang +4
Large language models (LLMs) increasingly serve as reasoners and automated evaluators, yet they remain susceptible to cognitive biases -- often altering their reasoning when faced…
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…
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
Meihan Liu, Zhen Zhang, Jiachen Tang +3
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the p…
A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading
Yuan Li, Bingqiao Luo, Qian Wang +3
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the u…
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Zhen Zhang, Meihan Liu, Anhui Wang +4
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. Howeve…
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…
AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective
Bingqiao Luo, Zhen Zhang, Qian Wang +3
In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficie…