https://github.com/simonschoelly/graphkernels.jl
A Julia package for kernel functions on graphs
https://github.com/simonschoelly/graphkernels.jl
graph-classification graph-kernels graph-similarity hacktoberfest julia kernel-functions kernels machine-learning
Last synced: 3 months ago
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A Julia package for kernel functions on graphs
- Host: GitHub
- URL: https://github.com/simonschoelly/graphkernels.jl
- Owner: simonschoelly
- License: mit
- Created: 2021-03-16T01:26:12.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-10-20T17:52:26.000Z (over 3 years ago)
- Last Synced: 2025-01-20T18:40:27.579Z (4 months ago)
- Topics: graph-classification, graph-kernels, graph-similarity, hacktoberfest, julia, kernel-functions, kernels, machine-learning
- Language: Julia
- Homepage:
- Size: 63.5 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GraphKernels.jl
A Julia package for calculating [graph kernels](https://en.wikipedia.org/wiki/Graph_kernel) -
kernel functions 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 classe 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
```
# Alternatives
## Graph Kernels
### Python
- [GraKeL](https://github.com/ysig/GraKeL), A scikit-learn compatible library for graph kernels
- [graphkit-learn](https://github.com/jajupmochi/graphkit-learn), A Python package for graph kernels, graph edit distances and graph pre-image problem.
## General graph machine learning
### Julia
- [GeometricFlux](https://github.com/FluxML/GeometricFlux.jl), Geometric Deep Learning for Flux
### Python
- [StellarGraph](https://github.com/stellargraph/stellargraph), Machine Learning on Graphs
- [DGL](https://github.com/dmlc/dgl), Python package built to ease deep learning on graph, on top of existing DL frameworks.
- [Graph Nets](https://github.com/deepmind/graph_nets), Build Graph Nets in Tensorflow
- [Spektral](https://github.com/danielegrattarola/spektral/), Graph Neural Networks with Keras and Tensorflow 2.
- [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric), Geometric Deep Learning Extension Library for PyTorch