  Graph Representation Learning (GRL) is an influential methodology, enabling a
more profound understanding of graph-structured data and aiding graph
clustering, a critical task across various domains. The recent incursion of
attention mechanisms, originally an artifact of Natural Language Processing
(NLP), into the realm of graph learning has spearheaded a notable shift in
research trends. Consequently, Graph Attention Networks (GATs) and Graph
Attention Auto-Encoders have emerged as preferred tools for graph clustering
tasks. Yet, these methods primarily employ a local attention mechanism, thereby
curbing their capacity to apprehend the intricate global dependencies between
nodes within graphs. Addressing these impediments, this study introduces an
innovative method known as the Graph Transformer Auto-Encoder for Graph
Clustering (GTAGC). By melding the Graph Auto-Encoder with the Graph
Transformer, GTAGC is adept at capturing global dependencies between nodes.
This integration amplifies the graph representation and surmounts the
constraints posed by the local attention mechanism. The architecture of GTAGC
encompasses graph embedding, integration of the Graph Transformer within the
autoencoder structure, and a clustering component. It strategically alternates
between graph embedding and clustering, thereby tailoring the Graph Transformer
for clustering tasks, whilst preserving the graph's global structural
information. Through extensive experimentation on diverse benchmark datasets,
GTAGC has exhibited superior performance against existing state-of-the-art
graph clustering methodologies.
