However, there is a lack of theoretical understanding of how mask-ing matters on graph autoencoders (GAEs). In this work, we present masked graph autoencoder (MaskGAE), a self …

However, there is a lack of theoretical understanding of how masking matters on graph autoencoders (GAEs). In this work, we present masked graph autoencoder (MaskGAE), a self …

Aug 4, 2023 · Generative self-supervised learning, exemplified by masked graph autoencoders (GAEs), aims to reconstruct the masked graph characteristics, garnering increasing research …

To address the limitations of existing node drop pooling methods, we design a masked graph auto-encoder constrained strategy called Masked Graph Auto-encoder constrained Pooling …

Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a …

Feb 9, 2023 · The solutions, based on autoregressive language modeling in GPT and masked autoencoding in BERT, are conceptually simple: they remove a portion of the data and learn to …

Following this philosophy, we propose masked graph autoencoder (MaskGAE), a self-supervised learning framework that leverages the idea of masking and predicting through the node and …

May 20, 2022 · In this work, we present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data. Different from standard GAEs, …

Nov 21, 2024 · Training and validation loss over 20 Masked Autoencoder (MAE) epochs. The consistent decline in both losses indicates stable learning, minimal overfitting, and effective …

We for-mulate the underlying data-generating process as a hierar-chical latent variable model, and show that under reason-able assumptions, MAE provably identifies a set of latent …