CalculateKStepRandomWalkKernel: k-step random walk kernel

In graphkernels: Graph Kernels

Description

This function calculates a kernel matrix of the k-step random walk kernel K_x^k.

Usage

CalculateKStepRandomWalkKernel(G, par)

Arguments

G	a list of igraph graphs
par	a vector of coefficients lambda_0, lambda_1, ..., lambda_k

Value

a kernel matrix of the k-step random walk kernel K_x^k

Author(s)

Mahito Sugiyama

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

data(mutag)
K <- CalculateKStepRandomWalkKernel(mutag, rep(1, 2))

Example output

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

graphkernels documentation built on Dec. 20, 2021, 9:07 a.m.