Supplementary Information: “Extracting the interdisciplinary specialty structures in social media data-based research: A clustering-based network approach”

dc.contributor.author	Fan, Yangliu
dc.contributor.author	Lehmann, Sune
dc.contributor.author	Blok, Anders
dc.date.accessioned	2025-02-27T13:02:25Z
dc.date.available	2025-02-27T13:02:25Z
dc.date.collected	2005/2019
dc.date.issued	2022
dc.description.abstract	In this Supplementary Information, we provide the additional analyses of authors and detail the results for network analysis that we present in the paper. The weighted network is generally represented as a graph G = (V, E, 𝜔) with a weight 𝜔 assigned to each edge, where V is the graph's vertex set, and E is the edge set. We assume that G is a simple undirected graph with no multiple edges or loops, and edges have no orientations. S1 Author-level analysis S2 Comparison of the two network filtering techniques S3 Network statistics S4 The disciplinary composition of clusters in the networks	en
dc.format	application/pdf
dc.identifier.uri	https://www.weizenbaum-library.de/handle/id/837
dc.identifier.url	https://ars.els-cdn.com/content/image/1-s2.0-S1751157722000621-mmc1.pdf
dc.relation.issupplementto	https://www.weizenbaum-library.de/handle/id/644
dc.rights	open access
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Bibliometrics
dc.subject	Interdisciplinarity
dc.subject	Social media data
dc.subject	Network science
dc.subject	Web of Science
dc.subject	Microsoft Academic Graph
dc.title	Supplementary Information: “Extracting the interdisciplinary specialty structures in social media data-based research: A clustering-based network approach”