Exploring Correlations of Self-supervised Tasks for Graphs

no code implementations • 7 May 2024 • Taoran Fang, Wei Zhou, Yifei Sun, Kaiqiao Han, Lvbin Ma, Yang Yang

Specifically, we evaluate the performance of the representations trained by one specific task on other tasks and define correlation values to quantify task correlations.

Multi-Task Learning Self-Supervised Learning

Universal Prompt Tuning for Graph Neural Networks

1 code implementation • NeurIPS 2023 • Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, Lei Chen

In this paper, we introduce a universal prompt-based tuning method called Graph Prompt Feature (GPF) for pre-trained GNN models under any pre-training strategy.

DropMessage: Unifying Random Dropping for Graph Neural Networks

2 code implementations • 21 Apr 2022 • Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, Yang Yang

First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models.

Graph Representation Learning