3 citations · 4 across the 6 of their papers we have counts for
6 papers
Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
Yancheng Chen, Dun Ma, Shuai Zhang +6
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning h…
Hard Constraints Meet Soft Generation: Guaranteed Feasibility for LLM-based Combinatorial Optimization
Yang Liu, Chuan Zhou, Yancheng Chen +3
Large language models (LLMs) have emerged as promising general-purpose solvers for combinatorial optimization (CO), yet they fundamentally lack mechanisms to guarantee solution fea…
CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding
Yang Liu, Chuan Zhou, Peng Zhang +4
Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the object…
Transformer-based Graph Neural Networks for Battery Range Prediction in AIoT Battery-Swap Services
Zhao Li, Yang Liu, Chuan Zhou +4
The concept of the sharing economy has gained broad recognition, and within this context, Sharing E-Bike Battery (SEB) have emerged as a focal point of societal interest. Despite t…
Decision-focused Graph Neural Networks for Combinatorial Optimization
Yang Liu, Chuan Zhou, Peng Zhang +3
In recent years, there has been notable interest in investigating combinatorial optimization (CO) problems by neural-based framework. An emerging strategy to tackle these challengi…
Combinatorial Optimization with Automated Graph Neural Networks
Yang Liu, Peng Zhang, Yang Gao +3
In recent years, graph neural networks (GNNs) have become increasingly popular for solving NP-hard combinatorial optimization (CO) problems, such as maximum cut and maximum indepen…