Multivariate social network visual analytics

Chris Muelder, Liang Gou, Kwan-Liu Ma, Michelle X. Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

One of the key research topics in Social Science and Sociology is to understand and analyze various social networks. Like any other types of networks, a social network consists of a set of nodes and links. Here, each node often represents a social entity, such as an individual or a group, and each link represents a particular relationship between two social entities. In a multivariate social network, each node/link can be associated with a set of properties, or there can even be multiple sets of heterogenous nodes or edges.

Original languageEnglish (US)
Title of host publicationMultivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions
PublisherSpringer-Verlag
Pages37-59
Number of pages23
ISBN (Print)9783319067926
DOIs
StatePublished - Jan 1 2014
Event3rd Dagstuhl Seminar on Information Visualization - Towards Multivariate Network Visualization - Saarland, Germany
Duration: May 12 2013May 17 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8380 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd Dagstuhl Seminar on Information Visualization - Towards Multivariate Network Visualization
CountryGermany
CitySaarland
Period5/12/135/17/13

Fingerprint

Visual Analytics
Social sciences
Social Networks
Vertex of a graph
Property of set
Social Sciences

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Muelder, C., Gou, L., Ma, K-L., & Zhou, M. X. (2014). Multivariate social network visual analytics. In Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions (pp. 37-59). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8380 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-06793-3_3

Multivariate social network visual analytics. / Muelder, Chris; Gou, Liang; Ma, Kwan-Liu; Zhou, Michelle X.

Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions. Springer-Verlag, 2014. p. 37-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8380 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Muelder, C, Gou, L, Ma, K-L & Zhou, MX 2014, Multivariate social network visual analytics. in Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8380 LNCS, Springer-Verlag, pp. 37-59, 3rd Dagstuhl Seminar on Information Visualization - Towards Multivariate Network Visualization, Saarland, Germany, 5/12/13. https://doi.org/10.1007/978-3-319-06793-3_3
Muelder C, Gou L, Ma K-L, Zhou MX. Multivariate social network visual analytics. In Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions. Springer-Verlag. 2014. p. 37-59. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-06793-3_3
Muelder, Chris ; Gou, Liang ; Ma, Kwan-Liu ; Zhou, Michelle X. / Multivariate social network visual analytics. Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions. Springer-Verlag, 2014. pp. 37-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{ff1479e521af4dbdac012e6d03a2cb65,
title = "Multivariate social network visual analytics",
abstract = "One of the key research topics in Social Science and Sociology is to understand and analyze various social networks. Like any other types of networks, a social network consists of a set of nodes and links. Here, each node often represents a social entity, such as an individual or a group, and each link represents a particular relationship between two social entities. In a multivariate social network, each node/link can be associated with a set of properties, or there can even be multiple sets of heterogenous nodes or edges.",
author = "Chris Muelder and Liang Gou and Kwan-Liu Ma and Zhou, {Michelle X.}",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-06793-3_3",
language = "English (US)",
isbn = "9783319067926",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "37--59",
booktitle = "Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions",

}

TY - GEN

T1 - Multivariate social network visual analytics

AU - Muelder, Chris

AU - Gou, Liang

AU - Ma, Kwan-Liu

AU - Zhou, Michelle X.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - One of the key research topics in Social Science and Sociology is to understand and analyze various social networks. Like any other types of networks, a social network consists of a set of nodes and links. Here, each node often represents a social entity, such as an individual or a group, and each link represents a particular relationship between two social entities. In a multivariate social network, each node/link can be associated with a set of properties, or there can even be multiple sets of heterogenous nodes or edges.

AB - One of the key research topics in Social Science and Sociology is to understand and analyze various social networks. Like any other types of networks, a social network consists of a set of nodes and links. Here, each node often represents a social entity, such as an individual or a group, and each link represents a particular relationship between two social entities. In a multivariate social network, each node/link can be associated with a set of properties, or there can even be multiple sets of heterogenous nodes or edges.

UR - http://www.scopus.com/inward/record.url?scp=84901278300&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901278300&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-06793-3_3

DO - 10.1007/978-3-319-06793-3_3

M3 - Conference contribution

AN - SCOPUS:84901278300

SN - 9783319067926

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 37

EP - 59

BT - Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions

PB - Springer-Verlag

ER -