A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Embedding-based Silhouette community detection
2020
Machine Learning
Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. This paper proposes the embedding-based Silhouette community detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the
doi:10.1007/s10994-020-05882-8
pmid:33191975
pmcid:PMC7652809
fatcat:zgzq44wugnbdrjrt3pc7libwke