Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
This work addresses graph analysis tasks for researchers and practitioners, but appears incremental as it builds on existing encoder and clustering techniques.
The paper tackles the problem of vertex embedding and community detection in graphs by introducing a graph encoder ensemble method, achieving excellent numerical performance as demonstrated through extensive simulations.
In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination. Our approach leverages a normalized one-hot graph encoder and a rank-based cluster size measure. Through extensive simulations, we demonstrate the excellent numerical performance of our proposed graph encoder ensemble algorithm.