How to cite DyNetVis

C. D. G. Linhares, J. R. Ponciano, J. G. S. Paiva, L. E. C. Rocha and B. A. N. Travençolo, "DyNetVis - An interactive software to visualize structure and epidemics on temporal networks," 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), The Hague, Netherlands, 2020, pp. 933-936, doi: 10.1109/ASONAM49781.2020.9381304. 

Abstract

The study of complex networks, especially temporal networks, increased over the last years. Understanding patterns, trends, and anomalies in these networks, as well as simulating and analyzing dynamic processes (e.g., infection spread dynamics in social networks), are not trivial tasks. Information Visualization techniques offer significant potential to assist the user in these analyses. This paper presents an extended version of Dynamic Network Visualization (DyNetVis), a freely available and open-source interactive software to perform visual analysis of temporal networks. It provides four visualization techniques, structural, temporal, matrix, and community layouts, and a number of state-of-the-art methods to interact with each of these layouts. DyNetVis also implements dynamic processes, including standard epidemic models. It is a computational tool to study and explore networks in diverse domains. 

Link to the paper

https://doi.org/10.1109/ASONAM49781.2020.9381304

BibTex

@INPROCEEDINGS{9381304,

  author={Linhares, Claudio D G and Ponciano, Jean R and Paiva, Jose Gustavo S and Rocha, Luis E C and Travençolo, Bruno A N},

  booktitle={2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, 

  title={DyNetVis - An interactive software to visualize structure and epidemics on temporal networks}, 

  year={2020},

  volume={},

  number={},

  pages={933-936},

  doi={10.1109/ASONAM49781.2020.9381304}}

How to cite DyNetVis version 1.0

Linhares, C. D. G., Rocha, L. E. C., Paiva, J. G. S., and Travençolo, B. A. N.  (2017). Dynetvis: A system for visualization of dynamic networks. Symposium on Applied Computing, pages 187–194.

Abstract

The concept of networks has been important in the study of complex systems. In networks, links connect pairs of nodes forming complex structures. Studies have shown that networks not only contain structure but may also evolve in time. The addition of the temporal dimension adds complexity on the analysis and requests the development of innovative methods for the visualization of real-life networks. In this paper we introduce the Dynamic Network Visualization System (DyNetVis), a software tool for visualization of dynamic networks. The system provides several tools for user interaction and offers two coordinated visual layouts, named structural and temporal. Structural refers to standard network drawing techniques, in which a single snapshot of nodes and links are placed in a plane, whereas the temporal layout allows for simultaneously visualization of several temporal snapshots of the dynamic network. In addition, we also investigate two approaches for temporal layout visualization: (i) Recurrent Neighbors, a node ordering strategy that highlights frequent connections in time, and (ii) Temporal Activity Map (TAM), a layout technique with focus on nodes activity. We illustrate the applicability of the layouts and interaction functionalities provided by the system in two visual analysis case studies, demonstrating their advantages to improve the overall user experience on visualization and exploratory data analysis on dynamic networks. 

Link to the article

https://doi.org/10.1145/3019612.3019686

BibTex

@inproceedings{Linhares:2017:DSV:3019612.3019686,

 author = {Linhares, Claudio D. G. and Traven\c{c}olo, Bruno A. N. and Paiva, Jose Gustavo S. and Rocha, Luis E. C.},

 title = {DyNetVis: A System for Visualization of Dynamic Networks},

 booktitle = {Proceedings of the Symposium on Applied Computing},

 series = {SAC '17},

 year = {2017},

 isbn = {978-1-4503-4486-9},

 location = {Marrakech, Morocco},

 pages = {187--194},

 numpages = {8},

 url = {http://doi.acm.org/10.1145/3019612.3019686},

 doi = {10.1145/3019612.3019686},

 acmid = {3019686},

 publisher = {ACM},

 address = {New York, NY, USA},

 keywords = {complex networks, dynamic graph visualization, dynamic networks, recurrent neighbors, temporal activity map},

} 

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