Hierarchical Graph Transformer with Adaptive Node Sampling

Understand

The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision.

• However, when it comes to graph-structured data, transformers have not achieved competitive performance, especially on large graphs.
• In this paper, we identify the main deficiencies of current graph transformers:(1) Existing node sampling strategies in Graph Transformers are agnostic to the graph characteristics and the training process.
• (2) Most sampling strategies only focus on local neighbors and neglect the long-range dependencies in the graph.