Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs.
Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as 'hairballs' that are difficult to understand, HB...
| Publicado en: | Connections (02261766) Vol. 40; no. 1; pp. 1 - 25 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Paradigm Publishing Services
2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=146645189&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 146645189 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02261766 DA5 jtl: Connections (02261766) issn: 02261766 maglogo: N pubinfo: dt: 2020 vid: 40 iid: 1 pid: 35924 pub: Paradigm Publishing Services artinfo: ui: 146645189 10.21307/connections-2019.009 ppf: 1 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 6.1MB tig: atl: Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs. aug: au: Allen, Patrick Matties, Mark Peterson, Elisha affil: Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA su: Centrality Representations of graphs Production standards sug: subj: Centrality Representations of graphs Production standards keyword: Comparing graphs Graph analytic triage Node-neighbor centrality Standard canonical form for graphs Comparing graphs Graph analytic triage Node-neighbor centrality Standard canonical form for graphs ab: Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as 'hairballs' that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node's neighbors' metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order M plus N log N, where N is the number of nodes and M is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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