Styling and minor update of README.md

README.md

@@ -4,33 +4,57 @@
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 ## Description
 
-Formally, a community detection aims to partition a graph’s vertices in subsets, such that there are many edges connecting between vertices of the same sub-set compared to vertices of different sub-sets; in essence, a community has many more ties between each constituent part than with outsiders. There are numerous algorithms present in the literature for solving this problem, a complete survey can be found in [1].  One of the popular community detection algorithms is presented in [2]. This algorithm separates the network in communities by optimizing greedily a modularity score after trying various grouping operations on the network. By using this simple greedy approach the algorithm is computationally very efficient.
-[1] Fortunato, Santo. "Community detection in graphs." Physics Reports 486, no. 3-5 (2010).
-[2] V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre. "Fast unfolding of communities in large networks." J. Stat. Mech., 2008: 1008.
+Formally, a community detection aims to partition a graph’s vertices in subsets, such that there are many edges connecting between vertices of the same sub-set compared to vertices of different sub-sets; in essence, a community has many more ties between each constituent part than with outsiders. There are numerous algorithms present in the literature for solving this problem, a complete survey can be found in [1].  
+
+One of the popular community detection algorithms is presented in [2]. This algorithm separates the network in communities by optimizing greedily a modularity score after trying various grouping operations on the network. By using this simple greedy approach the algorithm is computationally very efficient.
+
+[1] Fortunato, Santo. "Community detection in graphs." Physics Reports 486, no. 3-5 (2010).
+
+
+[2] V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre. "Fast unfolding of communities in large networks." J. Stat. Mech., 2008: 1008.
 
 ##Usage
 1. Import the script.
 
 		<script type="text/javascript" src="jLouvain.js"></script>
 
 2. Sample Data Format
-####Node Data
-		var node_data = ['id1', 'id2', 'id3']; // any type of string can be used as id
-####Edge Data
-		var edge_data = [{source: 'id1', target:'id2', weight: 10.0}, 
-						 {source: 'id2', target:'id3', weight: 20.0},
-						 {source: 'id3', target:'id1', weight: 30.0}];
-####(Optional) Partition Data
-		var init_part = {'id1':0, 'id2':0, 'id3': 1}; 
-		// Object with ids of nodes as properties and community number assigned as value.
+#### Node Data
+List of nodes ids. Any type of string can be used as id.
+
+```
+var node_data = ['id1', 'id2', 'id3'];
+```
+
+#### Edge Data
+```
+var edge_data = [
+	{source: 'id1', target:'id2', weight: 10.0}, 
+	{source: 'id2', target:'id3', weight: 20.0},
+	{source: 'id3', target:'id1', weight: 30.0}
+	];
+```
+
+#### (Optional) Partition Data
+Object with ids of nodes as properties and community number assigned as value.
+```
+var init_part = {'id1':0, 'id2':0, 'id3': 1}; 
+```
 
 3. Run the Algorithm on your node and edge set by chaining the **nodes** and **edges** methods, optionally you can provide an intermediary community partition assignement with the **partition_init** method. [ **Order of chaining is important** ]
+```
+var community = jLouvain().nodes(node_data).edges(edge_data).partition_init(init_part);
+var result  = community();
+```
+
+## Example
 
-		var community = jLouvain().nodes(node_data).edges(edge_data).partition_init(init_part);
-    	var result  = community();##Example
 See **example.html**, use the console to view the raw input data and raw output.
 
 Initial input graph for community detection.
-![](example/default.png)
-####After Community Detection
-We can see the partitioned graph vertices with the help of color coding.![](example/communities.png)
+![](example/default.png)
+
+#### After Community Detection
+
+We can see the partitioned graph vertices with the help of color coding.
+![](example/communities.png)