8 citations · 9 across the 5 of their papers we have counts for
12 papers
GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks
Jiarui Tan, Zhongjian Zhang, YaBo Guo +5
Large language model (LLM) agents are increasingly capable of planning, using tools, and interacting with external environments. They are typically supported by harnesses, which ma…
RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
Jinyu Yang, Cheng Yang, Junze Chen +4
Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) methods enable end-to-end pred…
Graph Foundation Models for Recommendation: A Comprehensive Survey
Bin Wu, Yihang Wang, Yuanhao Zeng +7
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role i…
Efficient Multi-task Prompt Tuning for Recommendation
Ting Bai, Le Huang, Yue Yu +4
With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In thi…
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Zhongjian Zhang, Xiao Wang, Huichi Zhou +4
Graph neural networks (GNNs) are vulnerable to adversarial attacks, especially for topology perturbations, and many methods that improve the robustness of GNNs have received consid…
Non-autoregressive Personalized Bundle Generation
Wenchuan Yang, Cheng Yang, Jichao Li +3
The personalized bundle generation problem, which aims to create a preferred bundle for user from numerous candidate items, receives increasing attention in recommendation. However…