tulip-python 5.7.2

Large graphs analysis and drawing

Module description

Graphs play an important role in many research areas, such as biology, microelectronics, social sciences, data mining, and computer science. Tulip (https://tulip.labri.fr) [1] [2] [3] is an Information Visualization framework dedicated to the analysis and visualization of such relational data. Written in C++ the framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations.

The Tulip core library is available to the Python community through the Tulip-Python bindings [4]. The bindings have been developed using the SIP tool [5] from Riverbank Computed Limited, allowing to easily create quality Python bindings for any C/C++ library. The main features provided by the bindings are the following ones:

• Creation and manipulation of graphs : Tulip provides an efficient graph data structure for storing large and complex networks. It is also one of the few that offer the possibility to efficiently define and navigate graph hierarchies or cluster trees (nested subgraphs).

• Storage of data on graph elements : Tulip allows to associate different kind of serializable data (boolean, integer, float, string, …) and visual attributes (layout, color, size, …) to graph elements. All these data can be easily accessed from the Tulip graph data structure facilitating the development of algorithms.

• Application of algorithms of different types on graph : Tulip has been designed to be easily extensible and provides a variety of graph algorithms (layout, metric, clustering, …) implemented as C++ plugins. All these algorithms can be called from Python. As Tulip is dedicated to graph visualization, it is provided with numerous state of the art graph layout algorithms but also a bridge to the Open Graph Drawing Framework (http://www.ogdf.net) [6]

References

[1]

David Auber, Daniel Archambault, Romain Bourqui, Maylis Delest, Jonathan Dubois, Antoine Lambert, Morgan Mathiaut, Patrick Mary, Guy Mélançon, Bruno Pinaud, Benjamin Renoust and Jason Vallet. TULIP 5. Encyclopedia of Social Network Analysis and Mining. 2017. https://hal.archives-ouvertes.fr/hal-01359308

[2]

David Auber, Romain Bourqui, Maylis Delest, Antoine Lambert, Patrick Mary, Guy Mélançon, Bruno Pinaud, Benjamin Renoust and Jason Vallet. TULIP 4. Research report. LaBRI - Laboratoire Bordelais de Recherche en Informatique. 2016. https://hal.archives-ouvertes.fr/hal-01359308

[3]

David Auber, Daniel Archambault, Romain Bourqui, Maylis Delest, Jonathan Dubois, Bruno Pinaud, Antoine Lambert, Morgan Mathiaut, Patrick Mary, and Guy Mélançon. Tulip III. Encyclopedia of Social Network Analysis and Mining. 2014. https://hal.archives-ouvertes.fr/hal-01096759

[4]

Antoine Lambert and David Auber. Graph analysis and visualization with Tulip-Python. EuroSciPy 2012 - 5th European meeting on Python in Science, Bruxelles. https://hal.archives-ouvertes.fr/hal-00744969

[5]

Riverbank Computing Limited. SIP - a tool for automatically generating Python bindings for C and C++ libraries. http://www.riverbankcomputing.co.uk/software/sip

[6]

M. Chimani, C. Gutwenger, M. Jünger, G. W. Klau, K. Klein, P. Mutzel. The Open Graph Drawing Framework (OGDF). Chapter 17 in: R. Tamassia (ed.), Handbook of Graph Drawing and Visualization, CRC Press, 2014. https://cs.brown.edu/~rt/gdhandbook/chapters/ogdf.pdf