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algorithms:percolationcentrality

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algorithms:percolationcentrality [2019/01/08 12:19]
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algorithms:percolationcentrality [2019/01/08 12:20] (current)
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 ^Input parameter ^Type ^Default ^Description ^ ^Input parameter ^Type ^Default ^Description ^
-|Node percolation state*|text|Compulsory|Percolation State of every node. Should be a possitive ​number.|+|Node percolation state*|text|Compulsory|Percolation State of every node. Should be a positive ​number.|
 |Distance|text|None|Link property acting as weight for the distance calculations. Must be positive numbers.| |Distance|text|None|Link property acting as weight for the distance calculations. Must be positive numbers.|
  * //Required Field//  * //Required Field//
  
-An example of usage would be the study of fire spreading in a forest. Imagine that trees are nodes, and two trees are linked when the fire can jump from one ot another. Given that some trees are initially burning (their percolation state would be 1.0, non-burning-trees would have 0.0), the percolation centrality of any tree would be its ability to spread the fire through the forest. So, if you are a fireman, you should focus on trees with high Percolation Centrality. This centrality can also be used in the field of rumor or information spreading in social networks.+An example of usage would be the study of fire spreading in a forest. Imagine that trees are nodes, and two trees are linked when the fire can jump from one to another. Given that some trees are initially burning (their percolation state would be 1.0, non-burning-trees would have 0.0), the percolation centrality of any tree would be its ability to spread the fire through the forest. So, if you are a fireman, you should focus on trees with high Percolation Centrality. This centrality can also be used in the field of rumor or information spreading in social networks.
  
 The following example shows a social network with the initial Percolation State (left) and the Percolation Centrality (right). As usual, warm colors indicate large values. We observe that the Percolation State is focused in one cluster, and that the Percolation Centrality highlights nodes that are bridges between groups (high betweenness) and also emphasizes nodes that are close to the source. Some small nodes that are marked with a red line are nodes with medium-high centrality that are not easy to find by visual inspection. The following example shows a social network with the initial Percolation State (left) and the Percolation Centrality (right). As usual, warm colors indicate large values. We observe that the Percolation State is focused in one cluster, and that the Percolation Centrality highlights nodes that are bridges between groups (high betweenness) and also emphasizes nodes that are close to the source. Some small nodes that are marked with a red line are nodes with medium-high centrality that are not easy to find by visual inspection.
algorithms/percolationcentrality.txt · Last modified: 2019/01/08 12:20 by systems