Gephi: An Open Source Software for Exploring and Manipulating Networks. Gephi is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs. Runs on Windows, Linux and Mac OS X. Gephi is open-source and free. Gephi is a tool for people that have to explore and understand graphs. Like Photoshop but for data, the user interacts with the representation, manipulate the structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypothesis, intuitively discover patterns, isolate structure singularities or faults during data sourcing. It is a complementary tool to traditional statistics, as visual thinking with interactive interfaces is now recognized to facilitate reasoning. This is a software for Exploratory Data Analysis, a paradigm appeared in the Visual Analytics field of research.

References in zbMATH (referenced in 17 articles )

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  1. Caron, François; Fox, Emily B.: Sparse graphs using exchangeable random measures (2017)
  2. De Luca, Felice; Di Giacomo, Emilio; Didimo, Walter; Kobourov, Stephen; Liotta, Giuseppe: An experimental study on the ply number of straight-line drawings (2017)
  3. D. Ranathunga, H. Nguyen, M. Roughan: MGtoolkit: A python package for implementing metagraphs (2017)
  4. Frahm, Klaus M.; El Zant, Samer; Jaffrès-Runser, Katia; Shepelyansky, Dima L.: Multi-cultural Wikipedia mining of geopolitics interactions leveraging reduced Google matrix analysis (2017)
  5. Kaplan, Andee; Hofmann, Heike; Nordman, Daniel: An interactive graphical method for community detection in network data (2017)
  6. Taylor, Dane; Myers, Sean A.; Clauset, Aaron; Porter, Mason A.; Mucha, Peter J.: Eigenvector-based centrality measures for temporal networks (2017)
  7. Alfonso Niño, Camelia Muñoz-Caro, Sebastián Reyes: APINetworks: A general API for the treatment of complex networks in arbitrary computational environments (2015)
  8. Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen: Unified functional network and nonlinear time series analysis for complex systems science: the pyunicorn package (2015)
  9. Fahimnia, Behnam; Tang, Christopher S.; Davarzani, Hoda; Sarkis, Joseph: Quantitative models for managing supply chain risks: a review (2015)
  10. Jonathan F. Donges, Jobst Heitzig, Boyan Beronov, Marc Wiedermann, Jakob Runge, Qing Yi Feng, Liubov Tupikina, Veronika Stolbova, Reik V. Donner, Norbert Marwan, Henk A. Dijkstra, J. Kurths: Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package (2015) arXiv
  11. Tsiotas, Dimitrios; Polyzos, Serafeim: Analyzing the maritime transportation system in Greece: a complex network approach (2015)
  12. Manitz, Juliane: Statistical inference for propagation processes on complex networks (2014)
  13. Palombi, Filippo; Toti, Simona: Stochastic dynamics of the multi-state voter model over a network based on interacting cliques and zealot candidates (2014)
  14. Ma, Yu-Xin; Xu, Jia-Yi; Peng, Di-Chao; Zhang, Ting; Jin, Cheng-Zhe; Qu, Hua-Min; Chen, Wei; Peng, Qun-Sheng: A visual analysis approach for community detection of multi-context mobile social networks (2013) ioport
  15. Nettleton, David F.: Data mining of social networks represented as graphs (2013)
  16. Purchase, Helen C.; Hamer, John; Nöllenburg, Martin; Kobourov, Stephen G.: On the usability of Lombardi graph drawings (2013)
  17. Fernandez, Maximiliano; Galeano, Javier; Hidalgo, Cesar: Bipartite networks provide new insights on international trade markets (2012)