Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse
  1. Ablage / 
  2. Datensatz ansehen

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
 ZurückNächste 
  Network Sparsification via Degree- and Subgraph-based Edge Sampling

Su, Z., Kurths, J., Meyerhenke, H. (2023): Network Sparsification via Degree- and Subgraph-based Edge Sampling. - In: An, J., Charalampo, C., Magdy, W. (Eds.), Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, Piscataway, NJ : Institute of Electrical and Electronics Engineers, 9-16.
https://doi.org/10.1109/ASONAM55673.2022.10068651

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://publications.pik-potsdam.de/pubman/item/item_28712 Versions-Permalink: https://publications.pik-potsdam.de/pubman/item/item_28712_1
Genre: Buchkapitel

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Su_2023_Network Sparsification via Degree- and Subgraph-based Edge Sampling.pdf (beliebiger Volltext), 624KB
 
Datei-Permalink:
-
Name:
Su_2023_Network Sparsification via Degree- and Subgraph-based Edge Sampling.pdf
Beschreibung:
-
Sichtbarkeit:
Privat
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Su, Zhen1, Autor              
Kurths, Jürgen1, Autor              
Meyerhenke, Henning 2, Autor
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Network (or graph) sparsification compresses a graph by removing inessential edges. By reducing the data volume, it accelerates or even facilitates many downstream analyses. Still, the accuracy of many sparsification methods, with filtering-based edge sampling being the most typical one, heavily relies on an appropriate definition of edge importance. Instead, we propose a different perspective with a generalized local-property-based sampling method, which preserves (scaled) local node characteristics. Apart from degrees, these local node characteristics we use are the expected (scaled) number of wedges and triangles a node belongs to. Through such a preservation, main complex structural properties are preserved implicitly. We adapt a game-theoretic framework from uncertain graph sampling by including a threshold for faster convergence (at least 4 times faster empirically) to approximate solutions. Extensive experimental studies on functional climate networks show the effectiveness of this method in preserving macroscopic to meso-scopic and microscopic network structural properties.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2023-03-232023-03-23
 Publikationsstatus: Final veröffentlicht
 Seiten: 8
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/ASONAM55673.2022.10068651
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
MDB-ID: yes - 3386
Research topic keyword: Complex Networks
Model / method: Nonlinear Data Analysis
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Genre der Quelle: Buch
 Urheber:
An, Jisun1, Herausgeber
Charalampo, Chelmis1, Herausgeber
Magdy, Walid1, Herausgeber
Affiliations:
1 External Organizations, ou_persistent22            
Ort, Verlag, Ausgabe: Piscataway, NJ : Institute of Electrical and Electronics Engineers
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 9 - 16 Identifikator: ISBN: 978-1-6654-5661-6
DOI: 10.1109/ASONAM55673.2022