  Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised
learning approach for graph-structured data. Existing GMAE models primarily
focus on reconstructing node-level information, categorizing them as
single-scale GMAEs. This methodology, while effective in certain contexts,
tends to overlook the complex hierarchical structures inherent in many
real-world graphs. For instance, molecular graphs exhibit a clear hierarchical
organization in the form of the atoms-functional groups-molecules structure.
Hence, the inability of single-scale GMAE models to incorporate these
hierarchical relationships often leads to their inadequate capture of crucial
high-level graph information, resulting in a noticeable decline in performance.
To address this limitation, we propose Hierarchical Graph Masked AutoEncoders
(Hi-GMAE), a novel multi-scale GMAE framework designed to handle the
hierarchical structures within graphs. First, Hi-GMAE constructs a multi-scale
graph hierarchy through graph pooling, enabling the exploration of graph
structures across different granularity levels. To ensure masking uniformity of
subgraphs across these scales, we propose a novel coarse-to-fine strategy that
initiates masking at the coarsest scale and progressively back-projects the
mask to the finer scales. Furthermore, we integrate a gradual recovery strategy
with the masking process to mitigate the learning challenges posed by
completely masked subgraphs. Diverging from the standard graph neural network
(GNN) used in GMAE models, Hi-GMAE modifies its encoder and decoder into
hierarchical structures. This entails using GNN at the finer scales for
detailed local graph analysis and employing a graph transformer at coarser
scales to capture global information. Our experiments on 15 graph datasets
consistently demonstrate that Hi-GMAE outperforms 17 state-of-the-art
self-supervised competitors.
