Tethne is a Python package for integrated bibliographic and corpus analysis developed by the Digital Innovation Group at Arizona State University. Tethne provides simple tools for generating networks from bibliographic datasets, and provides a framework for weaving together techniques from scientometrics, computational linguistics, topic modeling, and social influence modeling. Rather than reinvent or re- implement existing algorithms, Tethne is designed to interface with existing software packages, and to provide mechanisms for drawing the results and functionalities of those packages together.
Over 270 unit tests can be found in tethne-tests.
- Flexible core model for text and citation-data.
- Provides core analytic features of popular citation-analysis software (e.g. Citespace).
- Integrates popular topic modeling software (e.g. MALLET).
- Export network models to mainstream formats (e.g. for visualization in Cytoscape).
- Leverages powerful computational and network-analysis libraries in Python.
- HDF5 data management for persistence and interoperability.
For more information, see the Tethne website and documentation (under development).
The documentation project (ReST sources, etc.) can be found in tethne-docs.
We recommend using the Anaconda Python suite. See installation for details.
scipy==0.14.0
numpy==1.8.1
networkx==1.8.1
matplotlib==1.3.1
tables==3.1.1
Unidecode==0.04.16
geopy==0.99
nltk==2.0.4
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Tethne is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Tethne is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Tethne is developed by the ASU Digital Innovation Group (DigInG), part of the Laubichler Lab in the Center for Biology & Society, School of Life Sciences.
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2011131209, and NSF Doctoral Dissertation Research Improvement Grant No. 1256752.