1 code implementation • 18 Jan 2024 • Chenghua Gong, Yao Cheng, Xiang Li, Caihua Shan, Siqiang Luo
Graphs are structured data that models complex relations between real-world entities.
1 code implementation • 16 Oct 2023 • Chenghua Gong, Xiang Li, Jianxiang Yu, Cheng Yao, Jiaqi Tan, Chengcheng Yu
We first introduce asymmetric graph contrastive learning as pretext to address heterophily and align the objectives of pretext and downstream tasks.
no code implementations • 15 Oct 2023 • Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, Xuecang Zhang
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.