4 citations · 13 across the 15 of their papers we have counts for
15 papers
Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation
Jie Luo, Qi Jin, Xinming Zhang
Multi-modal Sequential Recommendation (SR) incorporates rich side information (e.g., textual and visual features) to enhance dynamic user preference modeling. However, existing fra…
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…
Time-Aware Adaptive Side Information Fusion for Sequential Recommendation
Jie Luo, Wenyu Zhang, Xinming Zhang +1
Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, des…
Event-Aware Prompt Learning for Dynamic Graphs
Xingtong Yu, Ruijuan Liang, Renhe Jiang +4
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural net…
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…
Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations
Wenyu Zhang, Jie Luo, Xinming Zhang +1
With the explosive growth of multimodal content online, pre-trained visual-language models have shown great potential for multimodal recommendation. However, while these models ach…
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…
Node-Time Conditional Prompt Learning In Dynamic Graphs
Xingtong Yu, Zhenghao Liu, Xinming Zhang +1
Dynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynami…
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…
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
Xingtong Yu, Yuan Fang, Zemin Liu +5
Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements. Earlier techniques often operate in an end-to-end setting, which heavily r…
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…
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
Xingtong Yu, Yuan Fang, Zemin Liu +1
Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their per…
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
Xingtong Yu, Zhenghao Liu, Yuan Fang +3
Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision. To reduce label…