Summary: GiGaMAE investigated how to enhance the generalization capability of self-supervised graph generative models, by reconstructing graph information in the latent space. They proposed a nove self-supervised reconstruction loss. Shi, Yucheng, et al. “Gigamae: Generalizable graph masked autoencoder via collaborative latent space reconstruction.” Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023. title: “[Article] Masked graph auto-encoder con...