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

Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method