22 citations · 25 across the 5 of their papers we have counts for
5 papers
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Li Sun, Zhenhao Huang, Yiding Wang +3
Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain ric…
Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity
Qin Chen, Guojie Song
Heterogeneous graphs (HGs) are common in real-world scenarios and often exhibit heterophily. However, most existing studies focus on either heterogeneity or heterophily in isolatio…
Large Language Models for Predictive Analysis: How Far Are They?
Qin Chen, Yuanyi Ren, Xiaojun Ma +1
Predictive analysis is a cornerstone of modern decision-making, with applications in various domains. Large Language Models (LLMs) have emerged as powerful tools in enabling nuance…
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach
Qin Chen, Liang Wang, Bo Zheng +1
The pre-train then fine-tune approach has advanced GNNs by enabling general knowledge capture without task-specific labels. However, an objective gap between pre-training and downs…
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
Xiaojun Ma, Qin Chen, Yuanyi Ren +2
Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream…