Practical Solutions for Scalable Graph Transformers
Introducing AnchorGT: A Novel Attention Architecture
Transformers have revolutionized machine learning, but faced challenges with graph data due to computational complexity. AnchorGT offers a solution to this scalability challenge while maintaining expressive power.
AnchorGT strategically selects “anchor” nodes to reduce computational burden, allowing each node to attend to its local neighbors and anchor nodes, capturing global information efficiently.
Using a concept from graph theory called the “k-dominating set”, AnchorGT ensures every node is at most k hops away from an anchor node, efficiently computed using a greedy algorithm.
AnchorGT variants of popular graph Transformer models demonstrated superior performance on various graph learning tasks, matching or exceeding original models while being more memory-efficient and faster.
This work balances computational efficiency and expressive power, making graph Transformers practical for large-scale graph data without compromising their strengths.
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