Graph Autoencoder with Community Neighborhood Network

Neighborhood information can be extracted from graph data structure. The neighborhood is valuable because similar objects tend to be connected. Graph neural networks (GNN) represent the neighborhood in layers depending on their proximity. Graph autoencoders (GAE) learn the lower dimensional representation of graph and reconstruct it afterward. The performance of the GAE might be enhanced with the behavior of GNNs. However, utilizing the neighborhood information is challenging. Far neighbors are capable of building redundantly complex networks due to their decreasing similarity. Yet, less neighborhood models are closer to GAE. Restricting the neighborhood within the same community enriches the GNN. In this work, we propose a new unsupervised model that combines GNN and GAE to improve the representation learning of graphs. We examine the outcomes of the model under different neighborhood configurations and hyperparameters. We also prove that the model is applicable to varying sizes and types of graphs within different categories on both synthetic and published datasets. The outcome of the community neighborhood network is resistant to overfitting with fewer learnable parameters.