Graph Kernels

Graph Kernels are learnable functions that map the graph structure into a lower dimensional representation and are commonly used to solve different problems in graph classification, or regression. In photonai-graph we utilize the grakel graph kernel package. We provide an adapter function that transforms connectivity matrices or networkx graphs into grakel conform graph objects.

GrakelAdapter

A transformer class for transforming graphs into Grakel format.

Parameters:

Name	Type	Description	Default
input_type	str	the type of the input, dense or networkx	'dense'
node_labels	dict	a list of the node labels, needed for shortest path kernel	None
edge_labels	dict	list of edge labels if graphs are constructed from networkx graphs	None
node_feature_construction	str	mode of feature construction for graphs constructed from adjacency matrices. "mean" takes the mean of the nodes edge weights, "sum" takes the sum of the nodes edge weights, "degree" takes the node degree, and "features" takes the nodes features as supplied in the feature matrix.	'mean'
adjacency_axis	int	position of the adjacency matrix, default being zero	0
feature_axis	int	position of the feature axis, default being 1	1

Example

Python

adapter = GrakelAdapter(input_type="dense", node_feature_construction="features")

Grakel Kernels

The available graph kernels include all currently available grakel graph kernels (see https://ysig.github.io/GraKeL/0.1a7/graph_kernel.html). Please make sure that you choose the right graph structure meaning that you supply node and edge labels or attributes are required by the specific kernel you are using. A guide on the recommended labels and features can be found in the link above.

For node labels set node_feature_construction to "mean", "sum" or "degree", for node attributes set it to "features".