Graph Clustering Using Node Embeddings: An Empirical Study
Paper i 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.


node representation (node embedding)

community detection

Graph data


Mahdi Ghanbari

Amirkabir University of Technology

Mostafa Haghir Chehreghani

Amirkabir University of Technology

Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science och AI

Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

9781665480451 (ISBN)

2022 IEEE International Conference on Big Data, Big Data 2022
Osaka, Japan,



Datavetenskap (datalogi)




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