Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jacomyal/abcd-gephi-plugin
Antagonism based Community Detection for Gephi
https://github.com/jacomyal/abcd-gephi-plugin
Last synced: about 1 month ago
JSON representation
Antagonism based Community Detection for Gephi
- Host: GitHub
- URL: https://github.com/jacomyal/abcd-gephi-plugin
- Owner: jacomyal
- License: mit
- Created: 2014-02-14T09:44:16.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2014-07-17T15:47:54.000Z (over 10 years ago)
- Last Synced: 2024-04-15T00:01:26.992Z (7 months ago)
- Language: Java
- Homepage:
- Size: 247 KB
- Stars: 5
- Watchers: 4
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
## Antagonism-based community detection
Developed by [Alexis Jacomy](https://github.com/jacomyal) for the [Contropedia](http://contropedia.net/) project.
Plugin released under the [MIT License](https://github.com/jacomyal/FriendshipsPlugin/blob/master/LICENSE.txt).
This Gephi plugins aims to find communities when edges are connecting enemy nodes.
Here is how it works:
1. The plugin computes a friendship score between each pair of nodes.
- The more common enemies two nodes have, the higher their score will be.
- The more connected two nodes are, the lower their score will become.
2. All original edges are removed.
3. Each pair of nodes that has a score higher than the threshold will become connected.
4. The Modularity algorithm is then used to detect communities, on the new graph.
5. Finally, unless the `Override edges` flag is set to true, the new edges are removed and the original ones are restored again. Be aware that if you set the `Override edges` flag the orginal network will be lost (unless you reload the orginal data).
Finally, you can represent the communities as colors through the `Partition > Nodes` panel (selecting Modularity).
### Details of the algortihm for calculation the friendship score:
#### Parameters of the Algorithm:
- **pd** = Direct Connection Penalty (should be a value below 0)
- **ct** = Common neighbours score (transitive node, `A->C->B`)
- **cg** = Common neighbours score (common traget node, `A->C<-B`)
- **co** = Common neighbours score (common source node, `A<-C->B`)
- **c** = Common neighbours score (undirected network, `A-C-B`). In the case of an undirected network the parameters ct, cg and co have to be set all to the same value (which we refer to as c from now on) otherwise the result of the algorithm will be depending on the internal representation of the undirected nodes in Gephi.
- **t** = treshold for the establishement of a friendship link
#### Example
Network: A, B, C, D nodes; w1, w2, w3, w4, w5 edge weights
````
D
/ \
w4 w5
/ \
A---w1---B
\ /
w2 w3
\ /
C
````
We calculate the friendship score `s` of A and B with the following formula (we only use an undirected network):
````
s(A, B) = pd * w1 + c * (w2 + w3 + w4 + w5)
````
And for A and C it is:
````
s(A, C) = pd * w2 + c * (w1 + w3)
````
All common neighbours of two nodes are contrasted with the weight multiplied by the penality factor `pd` of the conenctin between these two nodes.
A and B are linked if: `s(A, B) > t`