  Masked graph modeling excels in the self-supervised representation learning
of molecular graphs. Scrutinizing previous studies, we can reveal a common
scheme consisting of three key components: (1) graph tokenizer, which breaks a
molecular graph into smaller fragments (i.e., subgraphs) and converts them into
tokens; (2) graph masking, which corrupts the graph with masks; (3) graph
autoencoder, which first applies an encoder on the masked graph to generate the
representations, and then employs a decoder on the representations to recover
the tokens of the original graph. However, the previous MGM studies focus
extensively on graph masking and encoder, while there is limited understanding
of tokenizer and decoder. To bridge the gap, we first summarize popular
molecule tokenizers at the granularity of node, edge, motif, and Graph Neural
Networks (GNNs), and then examine their roles as the MGM's reconstruction
targets. Further, we explore the potential of adopting an expressive decoder in
MGM. Our results show that a subgraph-level tokenizer and a sufficiently
expressive decoder with remask decoding have a large impact on the encoder's
representation learning. Finally, we propose a novel MGM method SimSGT,
featuring a Simple GNN-based Tokenizer (SGT) and an effective decoding
strategy. We empirically validate that our method outperforms the existing
molecule self-supervised learning methods. Our codes and checkpoints are
available at https://github.com/syr-cn/SimSGT.
