# jLouvain 
**Corneliu S.**

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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.

##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.

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
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)