1 code implementation • 15 Apr 2023 • Bei Lin, You Li, Ning Gui, Zhuopeng Xu, Zhiwu Yu
However, partially due to the irregular non-Euclidean data in graphs, the pretext tasks are generally designed under homophily assumptions and cornered in the low-frequency signals, which results in significant loss of other signals, especially high-frequency signals widespread in graphs with heterophily.
1 code implementation • 3 Mar 2022 • You Li, Bei Lin, Binli Luo, Ning Gui
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.