26 citations · 47 across the 10 of their papers we have counts for
10 papers
Meta-Reflection: A Feedback-Free Reflection Learning Framework
Yaoke Wang, Yun Zhu, Xintong Bao +7
Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating ha…
Transferable and Forecastable User Targeting Foundation Model
Bin Dou, Baokun Wang, Yun Zhu +11
User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital mark…
GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
Yun Zhu, Haizhou Shi, Xiaotang Wang +5
Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancement…
Graph Retrieval-Augmented Generation: A Survey
Boci Peng, Yun Zhu, Yongchao Liu +5
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By ref…
E-CGL: An Efficient Continual Graph Learner
Jianhao Guo, Zixuan Ni, Yun Zhu +1
Continual learning has emerged as a crucial paradigm for learning from sequential data while preserving previous knowledge. In the realm of continual graph learning, where graphs c…
Graph Triple Attention Network: A Decoupled Perspective
Xiaotang Wang, Yun Zhu, Haizhou Shi +2
Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, t…
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks
Xiaotang Wang, Yun Zhu, Haizhou Shi +2
In the graph domain, deep graph networks based on Message Passing Neural Networks (MPNNs) or Graph Transformers often cause over-smoothing of node features, limiting their expressi…
Bridging Local Details and Global Context in Text-Attributed Graphs
Yaoke Wang, Yun Zhu, Wenqiao Zhang +3
Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in t…
Efficient Tuning and Inference for Large Language Models on Textual Graphs
Yun Zhu, Yaoke Wang, Haizhou Shi +1
Rich textual and topological information of textual graphs need to be modeled in real-world applications such as webpages, e-commerce, and academic articles. Practitioners have bee…
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
Yun Zhu, Yaoke Wang, Haizhou Shi +3
Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on th…