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  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

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_28712 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_28712_1
Genre: Book Chapter

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 Creators:
Su, Zhen1, Author              
Kurths, Jürgen1, Author              
Meyerhenke, Henning 2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2023-03-232023-03-23
 Publication Status: Finally published
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: 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
 Degree: -

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Title: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Source Genre: Book
 Creator(s):
An, Jisun1, Editor
Charalampo, Chelmis1, Editor
Magdy, Walid1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Piscataway, NJ : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 9 - 16 Identifier: ISBN: 978-1-6654-5661-6
DOI: 10.1109/ASONAM55673.2022