17 citations · 25 across the 7 of their papers we have counts for
7 papers
LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models
Mingqi Yang, Zidong Guo, Jihui Yang +1
Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictions through in-context learnin…
Think2Go: Generative Next POI Recommendation with LLM Reasoning
Zhuang Zhuang, Shanshan Feng, Hangwei Qian +4
Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next de…
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
Xuling Zhang, Jindong Li, Yifei Zhang +2
Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge.…
Paralleling and Accelerating Arc Consistency Enforcement with Recurrent Tensor Computations
Mingqi Yang
We propose a new arc consistency enforcement paradigm that transforms arc consistency enforcement into recurrent tensor operations. In each iteration of the recurrence, all involve…
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Mingqi Yang, Wenjie Feng, Yanming Shen +1
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Trans…
Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
Mingqi Yang, Yanming Shen, Heng Qi +1
For learning graph representations, not all detailed structures within a graph are relevant to the given graph tasks. Task-relevant structures can be or which…
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Mingqi Yang, Yanming Shen, Rui Li +3
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and t…