Feb 6, 2024 · Abstract:Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored.
May 13, 2024 · Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored.
May 22, 2024 · The paper proposes a new masked graph autoencoder model with structure-aware non-discrete bandwidths. The major idea is to change discrete masks to non-discrete ...
We propose a novel, informative, and effective topological masked graph autoencoder using bandwidth masking and a layer- wise bandwidth prediction objective. We ...
We explore a new paradigm of topological masked graph autoencoders with non-discrete masking strategies, named "bandwidths". We verify its effectiveness in ...
This paper proposes a novel, informative, and effective topological masked graph autoencoder using bandwidth masking and a layer-wise bandwidth prediction ...
Mar 14, 2024 · [rfp0338] Masked Graph Autoencoder with Non-discrete Bandwidths. 60 views · 9 months ago ...more. ACM SIGWEB. 1.07K. Subscribe.
Current GSP methods can be categorized into two primary streams: 1) Contrastive GSP methods, such as GraphCL [51] and SimGRACE [41], entail constructing ...
Feb 8, 2024 · Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we ...
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We present a masked graph autoencoder GraphMAE that mitigates these issues for generative self-supervised graph pretraining. Instead of reconstructing graph ...