graphkernels (version 1.6.1)

CalculateKStepRandomWalkKernel: k-step random walk kernel

Description

This function calculates a kernel matrix of the \(k\)-step random walk kernel \(K_{\times}^{k}\).

Usage

CalculateKStepRandomWalkKernel(G, par)

Arguments

G

a list of igraph graphs

par

a vector of coefficients \(\lambda_0, \lambda_1, \dots, \lambda_k\)

Value

a kernel matrix of the k-step random walk kernel \(K_{\times}^{k}\)

References

Gartner, T., Flach, P., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives, Learning Theory and Kernel Machines (LNCS 2777), 129-143 (2003) https://link.springer.com/chapter/10.1007/978-3-540-45167-9_11.

Sugiyama, M., Borgwardt, K. M.: Halting in Random Walk Kernels, Advances in Neural Information Processing Systems (NIPS 2015), 28, 1630-1638 (2015) https://papers.nips.cc/paper/5688-halting-in-random-walk-kernels.pdf.

Examples

# NOT RUN {
data(mutag)
K <- CalculateKStepRandomWalkKernel(mutag, rep(1, 2))
# }