Metadata-Version: 2.1
Name: stream-graph
Version: 0.2.0
Summary: A library for Stream Graphs
Home-page: https://ysig.github.io/GraKeL/dev/
Author: Yiannis Siglidis [LIP6]
Author-email: Yiannis.Siglidis@lip6.fr
License: GPLv3+
Project-URL: Documentation, https://ysig.github.io/stream_graph/doc
Project-URL: Lab Website, http://www.complexnetworks.fr/
Project-URL: Source, https://github.com/ysig/stream_graph
Project-URL: Tracker, https://github.com/ysig/stream_graph/issues
Description: 
        
        
        This library is an attempt at modelling Stream Graphs.
        A Stream Graph is a graph which nodes and links appear and disappear through time.
        Various methods that facilitate the study of such graphs can be found in this library, both simple (as degree distribution over time) and sophisticated (as maximal temporal-cliques, temporal-centrality measures).
        This library is hence designed for the analysis of the temporal dimension of evolving networks, such as the communication dynamics in social media. 
        
        Stream Graphs were first formally defined by [Matthieu Latapy *et al.*](https://hal.archives-ouvertes.fr/hal-01665084) as the generalization of static graphs.
        They consist of four components: (1) a set of nodes (`NodeSet`) belonging to the graph, (2) a time interval (`TimeSet`) representing the graph's lifespan, (3) a set of temporal nodes (`TemporalNodeSet`) describing instants when nodes are present in the stream, and (4) a set of temporal links (`TemporalLinkSet`) describing the instants when nodes are interacting in the stream.
        
        **Warning:** This library is currently **under development**. Elementary structures and methods may change, with no support for previous versions.
        
        
        ## Installing the Library
        
        Update: Version `0.2`
        ---------------------
        - Changes in interval-dataframe backbone:
          - Continuous (time) intervals.
          - Discrete (time) Intervals are treated differently
        - TODO: More Verification (add continuous bounds for maximal_cliques)
        
        
        Installing `stream_graph`
        -------------------------
        The `stream_graph` library requires:
        
        * Python [>=2.7, >=3.5]
        * Numpy [>=1.14.0]
        * Pandas [>=0.24.0]
        * Cython [>=0.27.3]
        * six [>=1.11.0]
        * Nose [>=1.3.0]
        * Cython [>=0.27.3]
        
        In order to allow visualizations, the user should install the latest `bokeh` library.
        
        ### Installing Dependencies
        
        
        To install dependencies:
        
        ```shell
        pip install extension>=extension_version
        ```
        
        Or more simply:
        
        ```shell
        pip install -r requirements.txt
        ```
        Please add `sudo` if `pip` does not have superuser privileges.
        
        
        ### Installing the `master` Version
        
        
        ```shell
        pip install git+https://github.com/ysig/stream_graph/
        ```
        
        
        ## Getting Started
        
        
        For a first introduction to the library, please have a look at [emailEU](https://nbviewer.jupyter.org/github/ysig/stream_graph/blob/master/tutorials/emailEU/email-Eu.ipynb) or visit the [tutorials page](https://ysig.github.io/stream_graph/doc/tutorials.html) within the documentation.
        
        
        ## Documentation
        
        
        The [library documentation](https://ysig.github.io/stream_graph/doc/) is available online and automatically generated with Sphinx.
        To generate it yourself, move to `doc` folder and execute: `make clean hmtl`, after having installed all the needed dependencies.
        
        ## Authors
        
        This package has been developed by researchers of the [Complex Networks](http://www.complexnetworks.fr/) team, within the [Computer Science Laboratory of Paris 6](https://www.lip6.fr/), for the [ODYCCEUS](https://www.odycceus.eu/) project, founded by the [European Commission FETPROACT 2016-2017 program](https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/calls/h2020-fetproact-2016-2017.html) under grant 732942.
        
        ### Contact
        * Yiannis Siglidis: <Yiannis.Siglidis@lip6.fr>
        * Robin Lamarche-Perrin: <Robin.Lamarche-Perrin@lip6.fr>
        
        ## License
        
        Copyright © 2019 [Complex Networks - LIP6](<http://www.complexnetworks.fr>)
        
        `stream_graph` 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. It 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 GN  General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Sociology
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4
Description-Content-Type: text/markdown
Provides-Extra: visualize
