updated the text a little more

paper/paper.md

@@ -33,14 +33,14 @@ Currently, there exists a number of tools for visualizing networks. *networkx* [
 
 For many users, these tools offer the necessary functionality to visualize networks in most desired ways. However, since a growing population of network researchers and practitioners are relying on Python for doing network science [@developersurvey], it is increasingly pressing that a fast and intuitive Python tool for network visualization exists.
 
-*Netwulf* is a light-weight Python library that provides an ultra simple API for interactively visualizing a network and returning the computed layout and style. It is build around the philosophy that network manipulation and preprocessing should be done programmatically, but that the efficient generation of a visually appealing network is best done interactively, without code. It allows users to:
+*Netwulf* is a light-weight Python library that provides an ultra simple API for interactively visualizing a network and returning the computed layout and style. It is build around the philosophy that network manipulation and preprocessing should be done programmatically, but that the efficient generation of a visually appealing network is best done interactively, without code. Therefore, it offers no analysis functionality and only few exploration features, but instead focuses almost entirely on fast and intuitive layout manipulation and node/link styling. Interaction with Netwulf typically works as follows:
 
-1. create a network, `G`, in either dictionary or *networkx.Graph* format, then calling `netwulf.visualize(G)`, to
-2. display it in a browser window as an interactive, manipulable, stylable network and 
-   1. post the final style and computed node-positions back to Python in dictionary format, allowing further manipulation, or redrawing with matplotlib [@matplotlib], or
-   2. save the image directly to the desktop as a PNG file.
+1. Users have a network, `G`, in either dictionary or *networkx.Graph* format. They then launch a Netwulf visualization by doing `netwulf.visualize(G)`.
+2. This opens a new browser window containing `G` as an interactive, manipulable, stylable network. Here the user can e.g. explore how different configurations of physics parameters like *node charge* and *gravity* influences the layout, they can change node-color, link alpha, etc. and even thresholds away weak or strong links. When the user has arrived at a layout they like, the can either:
+   1. Save the image directly from the interactive visualization to the desktop as a PNG file.
+   2. Post the style and computed node-positions back to Python in dictionary format. This allows for further manipulation. Moreover, using the function `netwulf.tools.draw_netwulf` the network can be redrawn with matplotlib [@matplotlib], which enables saving as any format.
 
-The visualization component is implemented in JavaScript, relies on d3.js [@d3] for computing layouts, and uses canvas for rendering. This makes it, to our knowledge, the fastest tool for network visualization in Python. 
+The interactive visualization is implemented in JavaScript, relies on d3.js [@d3] for computing layouts, and uses canvas for rendering. This makes it, to our knowledge, the fastest tool for network visualization in Python. 
 
 
 # Figures