Implementation of network diffusion algorithms such as insulated heat propagation or Markov random walks. Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs.
|Author||Simon Dirmeier [aut, cre]|
|Date of publication||2016-11-28 00:33:47|
|Maintainer||Simon Dirmeier <firstname.lastname@example.org>|
|License||GPL (>= 3)|
insulated.heat.diffusion: Graph diffusion using an insulated heat diffusion process
laplacian.heat.diffusion: Graph diffusion using a heat diffusion process on a Laplacian...
nearest.neighbors: Graph diffusion using nearest neighbors
normalize.laplacian: Calculate the Laplacian of a matrix
normalize.stochastic: Create a stochastically normalized matrix/vector
random.walk: Graph diffusion using a Markov random walk