Vgasom: community detection based on self-organizing map clustering of graph’s embeddings

In this paper, we proposed VGASOM, a neural network approach for community detection. Community detection refers to discovering similar nodes in a graph that form a community having similar features or attributes as opposed to nodes from other communities. The proposed approach combines the capabilities of auto-encoder neural networks, specifically a Variational graph auto-encoder (VGAE) with self-organizing maps (SOM) clustering. VGAEs have achieved great success in learning the latent representation of graphs and therefore encoding them into lower-dimensional embeddings. The self-organizing map based on competitive learning is used to find communities in the graphs’ embeddings obtained by the VGAE model which further reduces its dimensionality and divides the input space into clusters that correspond to the communities in the graph. We conducted experiments to evaluate our model compared to several baseline models, our model shows promising results for the community detection task. It outperforms the state-of-the-art methods by 3.29% in terms of the accuracy and 9% in terms of F1 metric.