Git repository for understanding A Clustered Approach for Fast Computation of Betweenness Centrality in Social Networks.
| Version | Date | Commit | Notes |
|---|---|---|---|
| 0.0 | April 28, 2021 | ec01299 | first commit |
| 0.1 | April 28, 2021 | 84d59e7 | Check border nodes |
| 0.2 | June 4, 2021 | 1351dcb | dist of two nodes to impact |
| 0.3 | June 6, 2021 | 55d5e59 | error ratio and computation time |
| 0.4 | June 6, 2021 | 3f8d100 | modified sorting order from ascending to descending |
| 0.5 | June 12, 2021 | eaaffb4 | calculate clustering coefficient |
The graph data files need to follow the rule below. <endpoint n> needs to be an int (node id)
<endpoint 1> <endpoint 2>
<endpoint 3> <endpoint 4>
.
.
.
Let's say there is a graph like this.
The following data (graph/simple_graph.gr) represents this simple graph with 9 nodes and 12 edges, which are <0, 1>, ..., <7, 8>.
0 1
0 2
1 2
1 3
2 3
2 7
3 4
3 5
4 6
5 6
5 8
7 8
Try the following command to get an instant result.
sh run.sh all graph/simple_graph.gr output/simple_graph.gr
- P. Suppa and E. Zimeo, A Clustered Approach for Fast Computation of Betweenness Centrality in Social Networks, 2015 IEEE International Congress on Big Data, 2015, pp. 47-54, doi: 10.1109/BigDataCongress.2015.17.