Update README.md with usage and description.

Author: Minyall

README.md

@@ -18,6 +18,10 @@ Then install squashy using PyPi
 pip install squashy
 ````
 
+## Usage
+See the `demos` folder for a Jupyter notebook showing a quick start guide to using Squashy. 
+A demo showing the individual component classes is coming soon.
+
 ## What does it do?
 
 At some point the key tools of network analysis struggle with scale. Beyond a few hundred nodes, graphs become too dense
@@ -29,4 +33,13 @@ settings or actually _see_ your data.
 ➡️Squashy⬅️can compress graphs made up of millions of nodes and edges into a graph of a few hundred nodes that retains the overall structure.
 
 ## How does it work?
-Demo and quick start code coming.
+Squashy is based on work by Natalie Stanley and colleagues published in _Scientific Reports_ ([DOI](http://dx.doi.org/10.1038/s41598-018-29174-3)).
+In short compression works by first identifying nodes in the network to be 'supernodes' or what we refer to in Squashy as 'CORE' nodes.
+These are determined by a process of K-Core decomposition, trimming away at the graph until all nodes have a degree of `k`, which is typically 2.
+Once this point is reached, the highest degree node is selected as a CORE, removed from the network and decomposition repeats until either there are no nodes left, or the desired number of CORES is reached.
+
+After decomposition, nodes are assigned to CORE nodes to represent them, through a process of breadth-first-search. Finally, new edges are created between nodes that represent the underlying edges of the nodes they represent.
+These edges are weighted to best represent the relationship between the nodes represented by two different cores. A graph formed of these core nodes and representative edges is our core graph, and should represent the underlying structure of the full raw graph.
+
+### Documentation?
+Please see the `demos` folder for a quickstart guide. Built-in documentation of the major classes and methods of Squashy is next on the list!