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A streaming edge sampling method for network visualization

Jean R. Ponciano, Claudio D. G. Linhares, Luis E C Rocha (UGent) , Elaine R. Faria and Bruno A. N. Travençolo
(2021) KNOWLEDGE AND INFORMATION SYSTEMS. 63(7). p.1717-1743
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Abstract
Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy and the network density, such layouts may suffer from a high level of visual clutter that hides meaningful temporal patterns, highly active groups of nodes, bursts of activity, and other important network properties. Edge sampling improves layout readability, highlighting important properties and leading to easier and faster pattern identification and decision making. This paper presents Streaming Edge Sampling for Network Visualization-SEVis, a streaming edge sampling method that discards edges of low-active nodes while preserving a distribution of edge counts that is similar to the original network. It can be applied to a variety of layouts to enhance streaming network analyses. We evaluated SEVis performance using synthetic and real-world networks through quantitative and visual analyses. The results indicate a higher performance of SEVis for clutter reduction and pattern identification when compared with other sampling methods.
Keywords
Human-Computer Interaction, Network Science, Visualisation, Data Science, Big Data, Information Systems, Temporal network, Edge sampling, Network visualization, Streaming network, Network sampling

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MLA
Ponciano, Jean R., et al. “A Streaming Edge Sampling Method for Network Visualization.” KNOWLEDGE AND INFORMATION SYSTEMS, vol. 63, no. 7, 2021, pp. 1717–43, doi:10.1007/s10115-021-01571-7.
APA
Ponciano, J. R., Linhares, C. D. G., Rocha, L. E. C., Faria, E. R., & Travençolo, B. A. N. (2021). A streaming edge sampling method for network visualization. KNOWLEDGE AND INFORMATION SYSTEMS, 63(7), 1717–1743. https://doi.org/10.1007/s10115-021-01571-7
Chicago author-date
Ponciano, Jean R., Claudio D. G. Linhares, Luis E C Rocha, Elaine R. Faria, and Bruno A. N. Travençolo. 2021. “A Streaming Edge Sampling Method for Network Visualization.” KNOWLEDGE AND INFORMATION SYSTEMS 63 (7): 1717–43. https://doi.org/10.1007/s10115-021-01571-7.
Chicago author-date (all authors)
Ponciano, Jean R., Claudio D. G. Linhares, Luis E C Rocha, Elaine R. Faria, and Bruno A. N. Travençolo. 2021. “A Streaming Edge Sampling Method for Network Visualization.” KNOWLEDGE AND INFORMATION SYSTEMS 63 (7): 1717–1743. doi:10.1007/s10115-021-01571-7.
Vancouver
1.
Ponciano JR, Linhares CDG, Rocha LEC, Faria ER, Travençolo BAN. A streaming edge sampling method for network visualization. KNOWLEDGE AND INFORMATION SYSTEMS. 2021;63(7):1717–43.
IEEE
[1]
J. R. Ponciano, C. D. G. Linhares, L. E. C. Rocha, E. R. Faria, and B. A. N. Travençolo, “A streaming edge sampling method for network visualization,” KNOWLEDGE AND INFORMATION SYSTEMS, vol. 63, no. 7, pp. 1717–1743, 2021.
@article{8712460,
  abstract     = {{Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy and the network density, such layouts may suffer from a high level of visual clutter that hides meaningful temporal patterns, highly active groups of nodes, bursts of activity, and other important network properties. Edge sampling improves layout readability, highlighting important properties and leading to easier and faster pattern identification and decision making. This paper presents Streaming Edge Sampling for Network Visualization-SEVis, a streaming edge sampling method that discards edges of low-active nodes while preserving a distribution of edge counts that is similar to the original network. It can be applied to a variety of layouts to enhance streaming network analyses. We evaluated SEVis performance using synthetic and real-world networks through quantitative and visual analyses. The results indicate a higher performance of SEVis for clutter reduction and pattern identification when compared with other sampling methods.}},
  author       = {{Ponciano, Jean R. and Linhares, Claudio D. G. and Rocha, Luis E C and Faria, Elaine R. and Travençolo, Bruno A. N.}},
  issn         = {{0219-1377}},
  journal      = {{KNOWLEDGE AND INFORMATION SYSTEMS}},
  keywords     = {{Human-Computer Interaction,Network Science,Visualisation,Data Science,Big Data,Information Systems,Temporal network,Edge sampling,Network visualization,Streaming network,Network sampling}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{1717--1743}},
  title        = {{A streaming edge sampling method for network visualization}},
  url          = {{http://doi.org/10.1007/s10115-021-01571-7}},
  volume       = {{63}},
  year         = {{2021}},
}
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