Abstract
The gradient descent has proven to be an effective optimization strategy. The current research proposes a novel clustering methodology using this strategy to recover communities in feature-rich networks. Our adoption of this strategy did not lead to promising results, and thus to improve them, we propose a special “refinement” mechanism, which culls out potentially misleading objects during the optimization. We validated and compared our proposed methods with three state-of-the-art algorithms over four real-world and 160 synthetic data sets. Our results proved that our proposed method is valid and in the majority of cases has a significant edge over the competitors.
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Shalileh, S., Mirkin, B. (2024). Community Detection in Feature-Rich Networks Using Gradient Descent Approach. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_15
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