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Hans-Jörg Schulz

Honeycomb: Visual Analysis of Large Scale Social Networks

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Honeycomb: Visual Analysis of Large Scale Social Networks. / Ham, Frank Van; Schulz, Hans-Jörg; Dimicco, Joan M.

In: Lecture Notes in Computer Science, Vol. 5727, 2009, p. 429-442.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

Harvard

Ham, FV, Schulz, H-J & Dimicco, JM 2009, 'Honeycomb: Visual Analysis of Large Scale Social Networks', Lecture Notes in Computer Science, vol. 5727, pp. 429-442. https://doi.org/10.1007/978-3-642-03658-3_47

APA

Ham, F. V., Schulz, H-J., & Dimicco, J. M. (2009). Honeycomb: Visual Analysis of Large Scale Social Networks. Lecture Notes in Computer Science, 5727, 429-442. https://doi.org/10.1007/978-3-642-03658-3_47

CBE

Ham FV, Schulz H-J, Dimicco JM. 2009. Honeycomb: Visual Analysis of Large Scale Social Networks. Lecture Notes in Computer Science. 5727:429-442. https://doi.org/10.1007/978-3-642-03658-3_47

MLA

Ham, Frank Van, Hans-Jörg Schulz and Joan M Dimicco. "Honeycomb: Visual Analysis of Large Scale Social Networks". Lecture Notes in Computer Science. 2009, 5727. 429-442. https://doi.org/10.1007/978-3-642-03658-3_47

Vancouver

Ham FV, Schulz H-J, Dimicco JM. Honeycomb: Visual Analysis of Large Scale Social Networks. Lecture Notes in Computer Science. 2009;5727:429-442. https://doi.org/10.1007/978-3-642-03658-3_47

Author

Ham, Frank Van ; Schulz, Hans-Jörg ; Dimicco, Joan M. / Honeycomb: Visual Analysis of Large Scale Social Networks. In: Lecture Notes in Computer Science. 2009 ; Vol. 5727. pp. 429-442.

Bibtex

@inproceedings{cefb803e79e548f4acae5e8b446bf257,
title = "Honeycomb: Visual Analysis of Large Scale Social Networks",
abstract = "The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.",
author = "Ham, {Frank Van} and Hans-J{\"o}rg Schulz and Dimicco, {Joan M}",
year = "2009",
doi = "10.1007/978-3-642-03658-3_47",
language = "English",
volume = "5727",
pages = "429--442",
journal = "Nyt fra Arbejdsministeriet",

}

RIS

TY - GEN

T1 - Honeycomb: Visual Analysis of Large Scale Social Networks

AU - Ham, Frank Van

AU - Schulz, Hans-Jörg

AU - Dimicco, Joan M

PY - 2009

Y1 - 2009

N2 - The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.

AB - The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.

U2 - 10.1007/978-3-642-03658-3_47

DO - 10.1007/978-3-642-03658-3_47

M3 - Conference article

VL - 5727

SP - 429

EP - 442

JO - Nyt fra Arbejdsministeriet

JF - Nyt fra Arbejdsministeriet

ER -