Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
CoRR(2024)
摘要
This paper introduces a refined graph encoder embedding method, enhancing the
original graph encoder embedding using linear transformation, self-training,
and hidden community recovery within observed communities. We provide the
theoretical rationale for the refinement procedure, demonstrating how and why
our proposed method can effectively identify useful hidden communities via
stochastic block models, and how the refinement method leads to improved vertex
embedding and better decision boundaries for subsequent vertex classification.
The efficacy of our approach is validated through a collection of simulated and
real-world graph data.
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