https://doi.org/10.1140/epjds/s13688-014-0027-8
Research
Fast filtering and animation of large dynamic networks
1
Max Planck Institute for Software Systems, Saarland University, Saarbrucken, Germany
2
Institute for Cross-Disciplinary Physics and Complex Systems, University of Balearic Islands, Palma de Mallorca, Spain
3
Yahoo! Research, Barcelona, Spain
4
Center for Complex Networks and Systems Research, Indiana University, Bloomington, USA
* e-mail: pms@mpi-sws.org
Received:
7
August
2013
Accepted:
17
September
2014
Published online:
25
October
2014
Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: (i) captures persistent trends in the structure of dynamic weighted networks, (ii) smoothens transitions between the snapshots of dynamic network, and (iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.
© The Author(s), 2014
