11 citations · 13 across the 5 of their papers we have counts for
5 papers
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Shuo Wang, Xiangyu Wang, Quanxin Wang +9
Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlyi…
Ask, and it shall be given: On the Turing completeness of prompting
Ruizhong Qiu, Zhe Xu, Wenxuan Bao +1
Since the success of GPT, large language models (LLMs) have been revolutionizing machine learning and have initiated the so-called LLM prompting paradigm. In the era of LLMs, peopl…
How to Make LMs Strong Node Classifiers?
Zhe Xu, Kaveh Hassani, Si Zhang +7
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tas…
Masked Graph Transformer for Large-Scale Recommendation
Huiyuan Chen, Zhe Xu, Chin-Chia Michael Yeh +4
Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes. However, t…
Graph Sanitation with Application to Node Classification
Zhe Xu, Boxin Du, Hanghang Tong
The past decades have witnessed the prosperity of graph mining, with a multitude of sophisticated models and algorithms designed for various mining tasks, such as ranking, classifi…