update readme

Author: sabroona

README.md

@@ -15,6 +15,8 @@ python -m pip install .
 
 ## Documentation
 
+The `development` branch provides all functionalities of the library, while the `main` branch provides only core functionalities for the user. The development branch is used to develop new functionalities for the library and evaluate the performance of community detection algorithms under the influence of different parameters. 
+
 `main.py` shows the examplary use of the Python library. Starting by generating a list of compounds, generating the similarity matrix and creating a graph based on the pairwise similarty measures. The different modification possibilieties are also showcased, like applying different thresholds and community detection algorithms as well as visualizations and export of images, graphml and csv files.
 
 Compounds can be generated by passing a recipe from `data/recipes.py` to the Compound class `classes/compound_generator.py`. Functionalities for the similarity matrix are provided by the Matrix class in `classes/matrix.py`. Graph objects can be generated and modified using the Graph class in `classes/graph.py`. To evaluate the performance of the community detection algorithms against the known community distribution of the generated compounds, the Scores class in `classes/scores.py` can be used. These results can be visualized in heatmaps or line plots using the Plots class in `classes/plots.py` after exporting them to a csv file. `data/compound_data.py` defines the different types of compounds and their corresponding sequence of letters to simulate the structure of e.g. proteins. `data/color_data.py` determines the color of the nodes in the graph according to the compound type represented by the node. The exports are stored in the `exports` folder and the Jupyter notebooks in the `experiments_helper` folder were used to automate the plotting of the exported results for the different experiments.