Abstract
We propose an extension of the celebrated K-means algorithm for community detection in feature-rich networks. Our least-squares criterion leads to a straightforward extension of the conventional batch K-means clustering method as an alternating optimization strategy for the criterion. By replacing the innate squared Euclidean distance with cosine distance we effectively tackle the so-called curse of dimensionality. We compare our proposed methods using synthetic and real-world data with state-of-the-art algorithms from the literature. The cosine distance-based version appears to be the overall winner, especially at larger datasets.
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Shalileh, S., Mirkin, B. (2022). An Extension of K-Means for Least-Squares Community Detection in Feature-Rich Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_24
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