Update Readme

* Add an example to README

* Add wikipedia link to README

Author: simonschoelly

README.md

@@ -2,4 +2,44 @@
 
 ![](https://img.shields.io/badge/lifecycle-experimental-orange.svg)
 
-A Julia package for kernel functions on graphs.
+A Julia package for calculating [graph kernels](https://en.wikipedia.org/wiki/Graph_kernel) -
+kernel function where the inputs are graphs.
+
+### Example
+
+```julia
+julia> using GraphKernels: ShortestPathGraphKernel, svmtrain, svmpredict
+
+julia> using GraphDatasets: loadgraphs, TUDatasets
+julia> using SimpleValueGraphs: get_graphval
+julia> using Random: shuffle
+julia> using Statistics: mean
+
+# load the MUTAG dataset - it contains 188 graphs of two different classes
+julia> graphs = loadgraphs(TUDatasets.MUTAGDataset(); resolve_categories=true)
+188-element ValGraphCollection of graphs with
+              eltype: Int8
+  vertex value types: (chem = String,)
+    edge value types: (bond_type = String,)
+   graph value types: (class = Int8,)
+
+# shuffle the graphs and split into train and test data
+julia> graphs = shuffle(graphs);
+julia> X_train, X_test = graphs[begin:120], graphs[121:end];
+julia> y_train, y_test = get_graphval.(X_train, :class), get_graphval.(X_test, :class);
+
+# instantiate a ShortestPathGraphKernel
+# dist_key is set to nothing so that we use unit distances for all edges
+julia> kernel = ShortestPathGraphKernel(;dist_key=nothing)
+ShortestPathGraphKernel{ConstVertexKernel}(0.0, ConstVertexKernel(1.0), nothing)
+
+# train a support vector machine with that kernel
+julia> model = svmtrain(X_train, y_train, kernel);
+
+# predict classed on the test data
+julia> y_test_pred = svmpredict(model, X_test);
+
+# compare with the actual classes and calculate the accuracy
+julia> accuracy = mean(y_test .== y_test_pred)
+0.8529411764705882
+```