Graph Clustering Using Node Embeddings: An Empirical Study

Paper in proceeding, 2022

A technique that has recently become popular to analyze graph data is node embedding learning. Many graph problems, such as node classification, link prediction and node clustering, can be solved using these embeddings. However, in the literature the efficiency of different embedding generation algorithms paired with different clustering algorithms is not extensively investigated. In this paper, we study the efficiency of well-known embedding generation algorithms in combination with clustering algorithms, to detect communities. We consider four embedding generation algorithms that use mechanisms such as convolution, attention, inductivity and shallowness; and three popular data clustering algorithms. Our experimental results reveal that the combination of GraphSAGE (that uses inductivity) with KMeans++ yields the best results. This can be due to high quality of embedding vectors generated by GraphSAGE and the regular shapes that the embeddings find in the vector space.