Publications

Main Publication

How to cite DyNetVis

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://dl.acm.org/citation.cfm?id=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},
} 

Other Publications (in english)

Computers & Graphics Journal - Special Section on SIBGRAPI 2019

Linhares, C. D. G., Ponciano, J.R., Pereira, F. S. F., Rocha, L. E. C., Paiva, J. G. S., and Travençolo, B. A. N. (2019). A scalable node ordering strategy based on community structure for enhanced temporal network visualization. Computers & Graphics Journal.


Abstract

Temporal networks have been used to map the structural evolution of social, technological, and biological systems, among others. Due to the large amount of information on real-world temporal networks, increasing attention has been given to issues related to the visual scalability of network visualization layouts. However, visual clutter due to edge overlap remains the main challenge calling for efficient methods to improve the visual experience. In this paper, we propose a novel and scalable node reordering approach for temporal network visualization, named Community-based Node Ordering (CNO), combining static community detection with node reordering techniques to enhance the identification of visual patterns. The perception of trends, periodicity, anomalies, and other temporal patterns, is facilitated, resulting in faster decision making. Our method helps not only the study of network activity patterns within communities but also the analysis of relatively large networks by breaking down its structure in smaller parts. Using CNO, we further propose a taxonomy to categorize activity patterns within communities. We performed a number of experiments and quantitative analyses using two real-world networks with distinct characteristics and showed that the proposed layout and taxonomy speed up the identification of patterns that would otherwise be difficult to see.

Link to the article

https://doi.org/10.1016/j.cag.2019.08.006

BibTex

@article{LINHARES2019185,
title = "A scalable node ordering strategy based on community structure for enhanced temporal network visualization",
journal = "Computers & Graphics",
volume = "84",
pages = "185 - 198",
year = "2019",
issn = "0097-8493",
doi = "https://doi.org/10.1016/j.cag.2019.08.006",
url = "http://www.sciencedirect.com/science/article/pii/S0097849319301347",
author = "Claudio D.G. Linhares and Jean R. Ponciano and Fabiola S.F. Pereira and Luis E.C. Rocha and Jose Gustavo S. Paiva and Bruno A.N. Traven\c{c}olo",
keywords = "Temporal networks, Dynamic networks, Network communities, Node reordering, Massive sequence view, Visual scalability"
}

Multimidia Tools and Applications Journal

Linhares, C. D. G., Ponciano, J.R., Pereira, F. S. F., Rocha, L. E. C., Paiva, J. G. S., and Travençolo, B. A. N. (2019). Visual analysis for evaluation of community detection algorithms. Multimidia Tools and Applications Journal.


Abstract

Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.

Link to the article

https://doi.org/10.1007/s11042-020-08700-4

BibTex

@article{LINHARES2020,
author = "Cláudio D. G. Linhares and Jean R. Ponciano and Fabíola S. F. Pereira and Luis E. C. Rocha and Jose Gustavo S. Paiva and Bruno A. N. Travençolo",
title = "Visual analysis for evaluation of community detection algorithms",
journal = "Multimedia Tools and Applications",
issn = "1573-7721",
year = "2020",
url = "https://doi.org/10.1007/s11042-020-08700-4",
doi = "10.1007/s11042-020-08700-4"
}

Temporal Network Theory - Book Chapter

Linhares, C. D. G., Ponciano, J.R., Rocha, L. E. C., Paiva, J. G. S., and Travençolo, B. A. N. (2019). Visualisation of structure and processes on temporal networks. Temporal Network Theory.


Abstract

The temporal dimension increases the complexity of network models but also provides more detailed information about the sequence of connections between nodes allowing a more detailed mapping of processes taking place on the network. The visualisation of such evolving structures thus permits faster identification of non-trivial activity patterns and provides insights about the mechanisms driving the dynamics on and of networks. In this chapter, we introduce key concepts and discuss visualisation methods of temporal networks based on 2D layouts where nodes correspond to horizontal lines with circles to represent active nodes and vertical edges connecting those active nodes at given times. We introduce and discuss algorithms to re-arrange nodes and edges to reduce visual clutter, layouts to highlight node and edge activity, and visualise dynamic processes on temporal networks. We illustrate the methods using real-world temporal network data of face-to-face human contacts and simulated random walk and infection dynamics.

Link to the bookchapter

http://dx.doi.org/10.1007/978-3-030-23495-9_5

BibTex

@Inbook{Linhares20192,
author="Linhares, Claudio D. G. and Ponciano, Jean R. and Paiva, Jose Gustavo S. and Traven\c{c}olo, Bruno A. N. and Rocha, Luis E. C.",
editor="Holme, Petter and Saram{\"a}ki, Jari",
title="Visualisation of Structure and Processes on Temporal Networks",
bookTitle="Temporal Network Theory",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="83--105",
isbn="978-3-030-23495-9",
doi="10.1007/978-3-030-23495-9\_5",
}

Other Publications (in portuguese)

WIM 2017 - CSBC

Linhares, C. D. G., Ponciano, J.R., Rocha, L. E. C., Paiva, J. G. S., and Travençolo, B. A. N. (2017). Análise temporal de uma rede de contato hospitalar utilizando técnicas de visualização de informação. In: 17º Workshop de Informática Médica - WIM2017, 2017, São Paulo - SP. CSBC.

Abstract

The visualization of temporal networks, i.e., the visualization of networks that represent interactions between a domain’s instances and that have information about when such interactions occur, plays a key role in the recognition of properties that would be difficult to perceive without an adequate visualization strategy. This paper presents an application case study of a visual analysis system in a hospital contact network between people. The goal is to demonstrate the applicability of this system in helping on decision making processes related to health data. The achieved results facilitate both the network analysis and the patterns perception, accelerating and making the decision making processes more reliable.

Download the article

http://www.lbd.dcc.ufmg.br/colecoes/wim/2017/001.pdf

BibTex

@inproceedings{Linhares:2017:WIM,
 author = {Linhares, Claudio D. G. and Ponciano, Jean R. and Traven\c{c}olo, Bruno A. N. and Paiva, Jose Gustavo S. and Rocha, Luis E. C.},
 title = {An\’alise temporal de uma rede de contato hospitalar utilizando t\’ecnicas de visualiza\c{c}\~ao de informa\c{c}\~ao},
 year = {2017},
 location = {S\~ao Paulo, Brasil},
 numpages = {10},
 publisher = {XVII Workshop de Inform\’atica M\’edica},
 keywords = {complex networks, dynamic graph visualization, dynamic networks, recurrent neighbors, temporal activity map},
}