Wavelet-Based Visual Analysis of Dynamic Networks

Alcebiades Dal Col, Paola Valdivia, Fabiano Petronetto, Fabio Dias, Claudio T. Silva, L. Gustavo Nonato

Research output: Contribution to journalArticlepeer-review


Dynamic networks naturally appear in a multitude of applications from different fields. Analyzing and exploring dynamic networks in order to understand and detect patterns and phenomena is challenging, fostering the development of new methodologies, particularly in the field of visual analytics. In this work, we propose a novel visual analytics methodology for dynamic networks, which relies on the spectral graph wavelet theory. We enable the automatic analysis of a signal defined on the nodes of the network, making viable the robust detection of network properties. Specifically, we use a fast approximation of a graph wavelet transform to derive a set of wavelet coefficients, which are then used to identify activity patterns on large networks, including their temporal recurrence. The coefficients naturally encode the spatial and temporal variations of the signal, leading to an efficient and meaningful representation. This methodology allows for the exploration of the structural evolution of the network and their patterns over time. The effectiveness of our approach is demonstrated using usage scenarios and comparisons involving real dynamic networks.

Original languageEnglish (US)
Pages (from-to)2456-2469
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number8
StatePublished - Aug 1 2018


  • Dynamic networks
  • spectral graph wavelets
  • visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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