UGMAE: A Unified Framework for Graph Masked Autoencoders

Generative self-supervised learning on graphs, particularly graph masked autoencoders, has emerged as a popular learning paradigm and demonstrated its efficacy in handling non-Euclidean data. However, several remaining issues limit the capability of existing methods: 1) the disregard of uneven node significance in masking, 2) the underutilization of holistic graph information, 3) the ignorance of semantic knowledge in the representation space due to the exclusive use of reconstruction loss in the output space, and 4) the unstable reconstructions caused by the …

autoencoders capability cs.ai cs.lg data framework generative graph graphs information masking node non-euclidean paradigm popular self-supervised learning significance supervised learning