Name
Abstract | ||
|---|---|---|
In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from practical points of view. In this contribution we present a module-based method to efficiently break complex networks. The procedure first identifies the communities in which the network can be represented, then it deletes the nodes (edges) that connect different modules by its order in the betweenness centrality ranking list. We illustrate the method by applying it to various well known examples of social, infrastructure, and biological networks. We show that the proposed method always outperforms vertex (edge) attacks which are based on the ranking of node (edge) degree or centrality, with a huge gain in efficiency for some examples. Remarkably, for the US power grid, the present method breaks the original network of 4941 nodes to many fragments smaller than 197 nodes (4% of the original size) by removing mere 164 nodes (~3%) identified by the procedure. By comparison, any degree or centrality based procedure, deleting the same amount of nodes, removes only 22% of the original network, i.e. more than 3800 nodes continue to be connected after that |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Network science,Combinatorics,Community structure,Ranking,Vertex (geometry),Biological network,Centrality,Betweenness centrality,Complex network,Artificial intelligence,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1502.00353 | 3 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Requião da Cunha | 1 | 4 | 1.78 |
| Juan Carlos González-Avella | 2 | 3 | 0.39 |
| Sebastián Gonçalves | 3 | 4 | 2.79 |