Gephi

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 62 articles )

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  1. Linderman, George C.; Steinerberger, Stefan: Dimensionality reduction via dynamical systems: the case of t-SNE (2022)
  2. Macías, Roman Zúñiga; Gutiérrez-Pulido, Humberto; Arroyo, Edgar Alejandro Guerrero; González, Abel Palafox: Geographical network model for COVID-19 spread among dynamic epidemic regions (2022)
  3. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  4. Alberto Garcia-Robledo, Mahboobeh Zangiabady: Dash Sylvereye: A WebGL-powered Library for Dashboard-driven Visualization of Large Street Networks (2021) arXiv
  5. Álvarez, Nicanor García; Adenso-Díaz, Belarmino; Calzada-Infante, Laura: Maritime traffic as a complex network: a systematic review (2021)
  6. Cheong, Se-Hang; Si, Yain-Whar; Wong, Raymond K.: Online force-directed algorithms for visualization of dynamic graphs (2021)
  7. De Vito, Roberta; Bellio, Ruggero; Trippa, Lorenzo; Parmigiani, Giovanni: Bayesian multistudy factor analysis for high-throughput biological data (2021)
  8. Gullotto, Danilo: Fine tuned exploration of evolutionary relationships within the protein universe (2021)
  9. Ardekani, Aref Mahdavi; Distinguin, Isabelle; Tarazi, Amine: Do banks change their liquidity ratios based on network characteristics? (2020)
  10. Benzi, Michele; Fika, Paraskevi; Mitrouli, Marilena: Performance and stability of direct methods for computing generalized inverses of the graph Laplacian (2020)
  11. Bombina, Polina; Ames, Brendan: Convex optimization for the densest subgraph and densest submatrix problems (2020)
  12. Candogan, Ozan; Drakopoulos, Kimon: Optimal signaling of content accuracy: engagement vs. misinformation (2020)
  13. Comin, Cesar H.; Peron, Thomas; Silva, Filipi N.; Amancio, Diego R.; Rodrigues, Francisco A.; Costa, Luciano da F.: Complex systems: features, similarity and connectivity (2020)
  14. Fardin Ghorbani, Soheil Hashemi, Ali Abdolali, Mohammad Soleimani: EEGsig machine learning-based toolbox for End-to-End EEG signal processing (2020) arXiv
  15. Marcus, Richard; Kohlhase, Michael; Rabe, Florian: TGView3D: a system for 3-dimensional visualization of theory graphs (2020)
  16. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  17. Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
  18. Taglieber, Klemens; Freiberg, Uta: Random graphs and their subgraphs (2020)
  19. Thiem, Christopher: Cross-category, trans-pacific spillovers of policy uncertainty and financial market volatility (2020)
  20. Weir, William H.; Walker, Benjamin; Zdeborová, Lenka; Mucha, Peter J.: Multilayer modularity belief propagation to assess detectability of community structure (2020)

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