PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer

Understand

Spectral Graph Neural Networks have demonstrated superior performance in graph representation learning.

• However, many current methods focus on employing shared polynomial coefficients for all nodes, i.e., learning node-unified filters, which limits the filters' flexibility for node-level tasks.
• The recent DSF attempts to overcome this limitation by learning node-wise coefficients based on positional encoding.
• However, the initialization and updating process of the positional encoding are burdensome, hindering scalability on large-scale graphs.