Abstract
Node embeddings present a powerful method of embedding graph-structured data into a low dimensional space while preserving local node information. Clustering is a common preprocessing task on unsupervised data utilized to get the best insight into the input dataset. The most prominent clustering algorithm is the K-Means algorithm. In this paper, we formulate clustering as an optimization problem using different objective functions following the idea of searching for the best fit centroid-based cluster exemplars. We also apply several nature-inspired optimization algorithms since the K-Means algorithm is trapped in local optima during its execution. We demonstrate our cluster frameworks’ capability on several graph clustering datasets used in node embeddings and node clustering tasks. Performance evaluation and comparison of our frameworks with the K-Means algorithm are demonstrated and discussed in detail. We end this paper with a discussion on the impact of the objective function’s choice on the clustering results.
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Alihodžić, A., Chahin, M., Čunjalo, F. (2022). New Clustering Techniques of Node Embeddings Based on Metaheuristic Optimization Algorithms. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, vol 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_23
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