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Since GNN lack of canonical positional information, it reduces the expressiveness of a GNN model and cannot reach ideal task performance. It is a must to support positional encoding for GNN model. Thus, it is a analog to Transformer model.
Since GNN lack of canonical positional information, it reduces the expressiveness of a GNN model and cannot reach ideal task performance. It is a must to support positional encoding for GNN model. Thus, it is a analog to Transformer model.
Learnable Structural Positional Encoding (LSPE) supports positional encoding learning from message-passing network.
Ref.
Graph Neural Networks with Learnable Structural and Positional Representations
https://github.com/vijaydwivedi75/gnn-lspe
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