Large Language Model Meets Graph Neural Network in Knowledge Distillation

no code implementations • 8 Feb 2024 • Shengxiang Hu, Guobing Zou, Song Yang, Yanglan Gan, Bofeng Zhang, Yixin Chen

Despite recent community revelations about the advancements and potential applications of Large Language Models (LLMs) in understanding Text-Attributed Graph (TAG), the deployment of LLMs for production is hindered by its high computational and storage requirements, as well as long latencies during model inference.

Contrastive Learning Knowledge Distillation +4

Structure-reinforced Transformer for Dynamic Graph Representation Learning with Edge Temporal States

no code implementations • 20 Apr 2023 • Shengxiang Hu, Guobing Zou, Song Yang, Shiyi Lin, Bofeng Zhang, Yixin Chen

The burgeoning field of dynamic graph representation learning, fuelled by the increasing demand for graph data analysis in real-world applications, poses both enticing opportunities and formidable challenges.

Dynamic Link Prediction Graph Representation Learning

Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization

no code implementations • 20 Aug 2017 • Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang

This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization.

regression