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  5. Reformulations of the Map Equation for Community Finding and Blockmodelling
 
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Reformulations of the Map Equation for Community Finding and Blockmodelling

Author(s)
Hurley, Neil J.  
Duriakova, Erika  
Uri
http://hdl.handle.net/10197/8413
Date Issued
2015-08-28
Date Available
2017-03-29T12:24:35Z
Abstract
Among the many community-finding algorithms that have been proposed in the last decade and more, the Infomapalgorithm of Rosvall and Bergstrom has proven among the best. The algorithm finds good community structure in directed aswell as undirected networks by abstracting information flow inthe network as a random walk. In this paper, we reformulate the objective in terms of the Kullback-Leibler distance between thedistribution of the random walk transitions and that of a modelwalk. The choice of model can be used to constrain the typeof partition that the method extracts. This generalisation makesthe method suitable for extracting other types of meso-structurefrom the network, enabling the analyst to explicitly control thetype of extracted structure.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2015 IEEE
Subjects

Machine learning

Statistics

Infomap method

Generalised blockmode...

DOI
10.1145/2808797.2809356
Language
English
Status of Item
Peer reviewed
Conference Details
The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), Paris, France, 25-28 August 2015
ISBN
9781450338547
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

Size

241.97 KB

Format

Adobe PDF

Checksum (MD5)

415bf59a548171badc0ed405fd4d9595

Owning collection
Insight Research Collection
Mapped collections
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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