Abstract
Community detection methods for the analysis of complex networks are increasingly important in modern literature. At the same time it is still an open problem. The approach proposed in this work is to adopt an ensemble procedure for obtaining a consensus matrix from which to perform a nonmetric MDS approach and then a clustering algorithm which allows to get a consensus partition of the nodes. The simulation study offers some interesting insights on the procedure because it shows that it is possible to understand the key nodes and the stable communities by considering different algorithms. The proposed approach is still applied to real data related to a network of patents.
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Acknowledgements
The authors wish to thank Ivan Cucco for proving the data related to the joint patent application network.
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Drago, C., Balzanella, A. (2015). Nonmetric MDS Consensus Community Detection. In: Morlini, I., Minerva, T., Vichi, M. (eds) Advances in Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-17377-1_11
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DOI: https://doi.org/10.1007/978-3-319-17377-1_11
Publisher Name: Springer, Cham
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