[Article] Gigamae: Generalizable graph masked autoencoder via collaborative latent space reconstruction.

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 constrained graph pooling.” date: 2024-03-13

Summary: MGAP is the novel node drop pooling method retaining sufficient effective graph information from both node attribute and network topology perspectives.

Liu, Chuang, et al. “Masked graph auto-encoder constrained graph pooling.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2022.