  Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has
demonstrated significant advantages in semi-supervised node prediction tasks
with improved computational efficiency and enhanced model robustness. However,
existing methods for processing local information either rely on sampling or
simple aggregation, which respectively result in the loss and squashing of
critical neighbor information.Moreover, the limited number of nodes in each
mini-batch restricts the model's capacity to capture the global characteristic
of the graph. In this paper, we propose LGMformer, a novel MGT model that
employs a two-stage augmented interaction strategy, transitioning from local to
global perspectives, to address the aforementioned bottlenecks.The local
interaction augmentation (LIA) presents a neighbor-target interaction
Transformer (NTIformer) to acquire an insightful understanding of the
co-interaction patterns between neighbors and the target node, resulting in a
locally effective token list that serves as input for the MGT. In contrast,
global interaction augmentation (GIA) adopts a cross-attention mechanism to
incorporate entire graph prototypes into the target node epresentation, thereby
compensating for the global graph information to ensure a more comprehensive
perception. To this end, LGMformer achieves the enhancement of node
representations under the MGT paradigm.Experimental results related to node
classification on the ten benchmark datasets demonstrate the effectiveness of
the proposed method. Our code is available at
https://github.com/l-wd/LGMformer.
