4 citations · 6 across the 8 of their papers we have counts for
9 papers
Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets
Xingtong Yu, Jiarun Zhou, Guanlin Ding +12
AI-based trading methods have rapidly evolved from machine learning and reinforcement learning to large language models (LLMs) and trading agents, yet their performance is still pr…
TradingMoE: Routing the Right Experts in Evolving Markets
Chang Zhou, Xingtong Yu, Minbin Huang +4
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can…
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
Xingtong Yu, Zhongwei Kuai, Chang Zhou +6
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…
Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights
Xingtong Yu, Shenghua Ye, Ruijuan Liang +4
Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in gr…
MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
Xingtong Yu, Chang Zhou, Xinming Zhang +1
Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook…
GCoT: Chain-of-Thought Prompt Learning for Graphs
Xingtong Yu, Chang Zhou, Zhongwei Kuai +2
Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raise…
SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation
Xingtong Yu, Zechuan Gong, Chang Zhou +2
Graphs are able to model interconnected entities in many online services, supporting a wide range of applications on the Web. This raises an important question: How can we train a…
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
Xingtong Yu, Chang Zhou, Yuan Fang +1
Given the ubiquity of graph data, it is intriguing to ask: Is it possible to train a graph foundation model on a broad range of graph data across diverse domains? A major hurdle to…
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Xingtong Yu, Chang Zhou, Yuan Fang +1
Graphs can inherently model interconnected objects on the Web, thereby facilitating a series of Web applications, such as web analyzing and content recommendation. Recently, Graph…