Ziang Li

Rethinking the Setting of Semi-supervised Learning on Graphs

2022 · arXiv (Cornell University)

We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In this paper, we highlight the significant influence of …