npm install louvain
---
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.
shell
npm i --save louvain
`
1. Import the package.
javascript
let louvain = require('louvain');
`
2. Sample Data Format
#### Node Data
javascript
let node_data = ['id1', 'id2', 'id3']; // any type of string can be used as id
`
#### Edge Data
javascript
let 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
javascript
let 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 ]
javascript
let community = louvain().nodes(node_data).edges(edge_data).partition_init(init_part);
let result = community();``#### After Community Detection
We can see the partitioned graph vertices with the help of color coding.
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