Rethinking the Setting of Semi-supervised Learning on Graphs

Ziang Li, Ming Ding, Weikai Li +4

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 th…