Models for Community Dynamics

Xu, G; Wu, Z; Cao, J; Tao, H

Models for Community Dynamics

Xu, G

Wu, Z Cao, J Tao, H

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Publisher:: Springer Reference
Publication Type:: Chapter
Citation:: Encyclopedia of Social Network Analysis and Mining, 2014, pp. 969 - 982
Issue Date:: 2014

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Field	Value	Language
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Wu, Z	en_US
dc.contributor.author	Cao, J	en_US
dc.contributor.author	Tao, H	en_US
dc.contributor.editor	Alhajj, R	en_US
dc.contributor.editor	Rokne, J	en_US
dc.date.issued	2014	en_US
dc.identifier.citation	Encyclopedia of Social Network Analysis and Mining, 2014, pp. 969 - 982	en_US
dc.identifier.isbn	978-1-4614-6169-2	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33809
dc.description.abstract	In the realm of network science, a complex network is a graph with non-trivial topological features. A social network, one of the important real-world complex networks, is usually be modeled as a graph, where the nodes are called actors and the edges connecting nodes are used to represent various ties. The dynamic network is usually defined as a sequence of snapshot graphs indexed by time. Community dynamics aims to process such dynamic network to produce a sequence of communities, that is, one community for each timestamp. Different from traditional community detection methods on the static network, community dynamics assumes to obtain communities of the current timestamp relies on the results of the previous timestamps.	en_US
dc.publisher	Springer Reference	en_US
dc.relation.ispartof	Encyclopedia of Social Network Analysis and Mining	en_US
dc.relation.isbasedon	10.1007/978-1-4614-6170-8_180	en_US
dc.title	Models for Community Dynamics	en_US
dc.type	Chapter
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.publication-status	Published	en_US

Abstract:

In the realm of network science, a complex network is a graph with non-trivial topological features. A social network, one of the important real-world complex networks, is usually be modeled as a graph, where the nodes are called actors and the edges connecting nodes are used to represent various ties. The dynamic network is usually defined as a sequence of snapshot graphs indexed by time. Community dynamics aims to process such dynamic network to produce a sequence of communities, that is, one community for each timestamp. Different from traditional community detection methods on the static network, community dynamics assumes to obtain communities of the current timestamp relies on the results of the previous timestamps.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33809