Compute diffusion kernel-based distance matrix
Using a diffusion kernel-based algorithm, compute the distance between vertex pairs in an undirected network, with or without edge weights. This algorithm provides an alternative to the
mfpt measures of vertex pair distance.
String, the name of the edge attribute to be used as weights along the edges. Greater weights indicate a stronger interaction between the two genes (this is the opposite to edge distances, where smaller distances indicate stronger interactions). If
Numeric value, the probability that the diffusion process will take an edge emanating from a vertex.
Diffusion across a network follows a process similar to a random walk. This provides a method of measuring the distance between vertex pairs that does not simply take into account a single path (like the
shortest.paths algorithm) but instead incorporates multiple paths. This function uses a diffusion kernel-based approach to compute distances. The algorithm implemented is detailed in the referenced paper.
The distance from vertex A to vertex A is always
Numeric matrix, containing the diffusion kernel-based vertex pair distances between each vertex in
v and every vertex in
Alex J. Cornish firstname.lastname@example.org
Kondor, R.I. and Lafferty, J. (2002). Diffusion Kernels on Graph and Other Discrete Structures. Proc. Intl. Conf. Machine Learning.
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