kernels: Compute graph kernels
In b2slab/diffusion: Diffusion scores on biological networks

Description Usage Arguments Details Value References Examples

Function commuteTimeKernel computes the conmute-time kernel, which is the expected time of going back and forth between a couple of nodes. If the network is connected, then the commute time kernel will be totally dense, therefore reflecting global properties of the network. For further details, see [Yen, 2007]. This kernel can be computed using both the unnormalised and normalised graph Laplacian.

Function diffusionKernel computes the classical diffusion kernel that involves matrix exponentiation. It has a "bandwidth" parameter σ^2 that controls the extent of the spreading. Quoting [Smola, 2003]: K(x1,x2) can be visualized as the quantity of some substance that would accumulate at vertex x2 after a given amount of time if we injected the substance at vertex x1 and let it diffuse through the graph along the edges. This kernel can be computed using both the unnormalised and normalised graph Laplacian.

Function inverseCosineKernel computes the inverse cosine kernel, which is based on a cosine transform on the spectrum of the normalized Laplacian matrix. Quoting [Smola, 2003]: the inverse cosine kernel treats lower complexity functions almost equally, with a significant reduction in the upper end of the spectrum. This kernel is computed using the normalised graph Laplacian.

Function pStepKernel computes the p-step random walk kernel. This kernel is more focused on local properties of the nodes, because random walks are limited in terms of length. Therefore, if p is small, only a fraction of the values K(x1,x2) will be non-null if the network is sparse [Smola, 2003]. The parameter a is a regularising term that is summed to the spectrum of the normalised Laplacian matrix, and has to be 2 or greater. The p-step kernels can be cheaper to compute and have been successful in biological tasks, see the benchmark in [Valentini, 2014].

Function regularisedLaplacianKernel computes the regularised Laplacian kernel, which is a standard in biological networks. The regularised Laplacian kernel arises in numerous situations, such as the finite difference formulation of the diffusion equation and in Gaussian process estimation. Sticking to the heat diffusion model, this function allows to control the constant terms summed to the diagonal through add_diag, i.e. the strength of the leaking in each node. If a node has diagonal term of 0, it is not allowed to disperse heat. The larger the diagonal term of a node, the stronger the first order heat dispersion in it, provided that it is positive. Every connected component in the graph should be able to disperse heat, i.e. have at least a node i with add_diag[i] > 0. If this is not the case, the result diverges. More details on the parameters can be found in [Smola, 2003]. This kernel can be computed using both the unnormalised and normalised graph Laplacian.

commuteTimeKernel(graph, normalized = FALSE)

diffusionKernel(graph, sigma2 = 1, normalized = TRUE)

inverseCosineKernel(graph)

pStepKernel(graph, a = 2, p = 5L)

regularisedLaplacianKernel(graph, sigma2 = 1, add_diag = 1, normalized = FALSE)

`graph`	undirected igraph object. If the edges have weights, those should typically be non-negative.
`normalized`	logical, should the normalised (`TRUE`) or unnormalised (`FALSE`) graph Laplacian matrix be used?
`sigma2`	numeric value, parameter σ^2 of the kernel - higher values force more spreading in the network
`a`	numeric value greater or equal to 2, which acts as a regularisation term. Can also be a vector of length `vcount(graph)`
`p`	integer greater than 0, the number of steps for the random walk
`add_diag`	numeric value or vector of length `vcount(graph)`, term to regularise the spectrum of the Laplacian

Please be aware that the kernel computation can be rather slow and memory demanding. This is a reference table of the peak memory usage and computing time for the regularised Laplacian kernel given the order of the network:

5k: 900MB & 250s

10k: 3,200MB & 2,200s

15k: 8,000MB & 8,000s

20k: 13,000MB & 21,000s

However, given a network to study, this step is a one-time task than can be stored and reused.

A kernel matrix with adequate dimnames

The regularised Laplacian, diffusion, p-step and inverse cosine kernels: Smola, A. J., & Kondor, R. (2003, August). Kernels and regularization on graphs. In COLT (Vol. 2777, pp. 144-158).

The commute time kernel: Yen, L., Fouss, F., Decaestecker, C., Francq, P., & Saerens, M. (2007). Graph nodes clustering based on the commute-time kernel. Advances in Knowledge Discovery and Data Mining, 1037-1045.

Benchmark on kernels: Valentini, G., Paccanaro, A., Caniza, H., Romero, A. E., & Re, M. (2014). An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods. Artificial Intelligence in Medicine, 61(2), 63–78.

data(graph_toy)
K_lap <- regularisedLaplacianKernel(graph_toy)
K_diff <- diffusionKernel(graph_toy)
K_pstep <- pStepKernel(graph_toy)
K_ct <- commuteTimeKernel(graph_toy)
K_ic <- inverseCosineKernel(graph_toy)
is_kernel(K_lap)