Graph Neural Networks Powered by Encoder Embedding for Improved Node Clustering

Music City Center 

Graph Neural Networks (GNNs) have demonstrated an exceptional ability to model relationships within graph data, achieving remarkable results on tasks such as node clustering, node classification, and link prediction. However, most existing approaches rely on arbitrary or simplistic node embedding initialization, which can yield slow convergence and degrade performance. To address these challenges, we introduce a GEE-driven GNN (GG), which employs One-Hot Graph Encoder Embedding (GEE) to provide structured and expressive initialization for the GNN. We evaluate GG on node clustering tasks and simulations show that it converges faster and achieves superior results on certain graphs. Moreover, experiments on real-world datasets further demonstrate GG's strong performance, highlighting its potential as a powerful tool for diverse graph-related applications.

Graph Neural Networks

Graph Embedding

One-Hot Encoding

Node Features

Node Clustering 

Section on Statistical Learning and Data Science