1 citations · 1 across the 3 of their papers we have counts for
4 papers
Revisiting Graph Autoencoders as Implicit Contrastive Learners
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs, yet they are often studied in isolatio…
L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
Xinzhou Jin, Jintang Li, Liang Chen +6
Graph neural networks (GNNs) have recently emerged as an effective approach to model neighborhood signals in collaborative filtering. Towards this research line, graph contrastive…
State Space Models on Temporal Graphs: A First-Principles Study
Jintang Li, Ruofan Wu, Xinzhou Jin +3
Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors.…
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm
Yuanzhen Xie, Xinzhou Jin, Tao Xie +7
In-context learning of large-language models (LLMs) has achieved remarkable success in the field of natural language processing, while extensive case studies reveal that the single…