Laplacian PE
Laplacian Positional Encodings
[Laplacian eigenvectors](https://paperswithcode.com/paper/laplacian-eigenmaps-and-spectral-techniques) represent a natural generalization of the [Transformer](https://paperswithcode.com/method/transformer) positional encodings (PE) for graphs as the eigenvectors of a discrete line (NLP graph) are the cosine and sinusoidal functions. They help encode distance-aware information (i.e., nearby nodes have similar positional features and farther nodes have dissimilar positional features).

Hence, Laplacian Positional Encoding (PE) is a general method to encode node positions in a graph. For each node, its Laplacian PE is the k smallest non-trivial eigenvectors.
Benchmarking Graph Neural Networks
https://paperswithcode.com/paper/benchmarking-graph-neural-networks
Benchmarking Graph Neural Networks