The novelty and technical contribution are limited.
It is unclear for the deformable graph attention module.
It is unclear why the proposed method has lower computational complexity.
Detailed comments:
What is the motivation to choose personalized pagerank score, bfs, and feature similarity as sorting criteria?
For NodeSort, 1) how to choose the base node, or is every node a base node? 2)“NodeSort differentially sorts nodes depending on the base node.” Does this mean that the base node affects the ordering, affects the key nodes for attention, and further affects the model performance? 3)After getting the sorted node sequence, how to sample the key nodes for each node? And how many key nodes are sampled？is the number of key nodes a hyper-parameter? 4)What are the Value nodes used in Transformer in this paper? 5)How to fuse node representations generated by attention for different ranking criteria.
Intuitively, the design of deformable graph attention is complicated, and the Katz positional encoding involves the exponentiation of adjacency matrix, so Is the computational complexity really reduced? Where can the reduction in complexity be explained from the proposed method compared to baselines? or just from the sparse implementation?
2)“NodeSort differentially sorts nodes depending on the base node.” Does this mean that the base node affects the ordering, affects the key nodes for attention, and further affects the model performance?