The projection method: a unified formalism for community detection

Autor: Martijn Gösgens, Remco van der Hofstad, Nelly Litvak

Publikováno v: Frontiers in Complex Systems, Vol 2 (2024)

We present the class of projection methods for community detection that generalizes many popular community detection methods. In this framework, we represent each clustering (partition) by a vector on a high-dimensional hypersphere. A community detec

Externí odkaz: https://doaj.org/article/675c67bcb3f241f984d0b92d9d8217c6

From free text to clusters of content in health records: an unsupervised graph partitioning approach

Autor: M. Tarik Altuncu, Erik Mayer, Sophia N. Yaliraki, Mauricio Barahona

Publikováno v: Applied Network Science, Vol 4, Iss 1, Pp 1-23 (2019)

Abstract Electronic healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a

Externí odkaz: https://doaj.org/article/9f04e59db9584958944415359fa2f06f

Generalized Markov stability of network communities

Autor: Giulio Cimini, Aurelio Patelli, Andrea Gabrielli

Publikováno v: Physical review. E
info:cnr-pdr/source/autori:Patelli, Aurelio; Gabrielli, Andrea; Cimini, Giulio/titolo:Generalized Markov stability of network communities/doi:10.1103%2FPhysRevE.101.052301/rivista:Physical review. E (Print)/anno:2020/pagina_da:052301-1/pagina_a:052301-11/intervallo_pagine:052301-1–052301-11/volume:101

We address the problem of community detection in networks by introducing a general definition of Markov stability, based on the difference between the probability fluxes of a Markov chain on the network at different time scales. The specific implemen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39b018d056443c3dec0224c04365c371

From free text to clusters of content in health records: An unsupervised graph partitioning approach

Autor: Altuncu, M. Tarik, Mayer, Erik, Yaliraki, Sophia N., Barahona, Mauricio

Publikováno v: Applied Network Science, Vol 4, Iss 1, Pp 1-23 (2019)
Applied Network Science

Electronic Healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::170c194e3693908e885fae9570dd9dd5
http://hdl.handle.net/10044/1/66234

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