JGNN

A native Java library for Graph Neural Networks.

:cyclone: Changes from 1.0.0

• Neuralang: a scripting language for neural network declaration
• Autosized parameteters
• Up to 30% less memory
• Up to 50% less running time
• Renamed GCNBuilder to FastBuilder
• Neighbor attention and message passing
• Sort pooling and graph classification

:dart: About

Graph Neural Networks (GNNs) are getting more and more popular; they can reason from relational information and perform inference from small datasets. JGNN implements GNNs for native Java applications to supports cross-platform machine learning, such as on Android, without dedicated hardware or firmware.

• Tutorials
• Javadoc

:rocket: Features

• Cross-platform (written in native java)
• Lightweight
• Minimal memory footprint
• A scripting language for model definition
• Parallelized batching

:zap: Quickstart

To install the latest working version, you can include it as a Maven or Gradle dependency by following the instructions of its JitPack distribution:

As a good out-of-the-box graph neural network for node classification, you can try the following architecture. To create your own graph adjacency matrices, node features, and labels look at the data creation tutorial.

Dataset dataset = new Cora();
Matrix adjacency = dataset.graph().setMainDiagonal(1).setToSymmetricNormalization();
Matrix nodeFeatures = dataset.features();
Matrix nodeLabels = dataset.labels();
Slice nodes = dataset.samples().getSlice().shuffle(100);
long numClasses = nodeLabels.getCols();

ModelBuilder modelBuilder = new FastBuilder(adjacency, nodeFeatures)
        .config("reg", 0.005)
        .config("hidden", 16)
        .config("classes", numClasses)
        .layer("h{l+1}=relu(h{l}@matrix(features, hidden, reg)+vector(hidden))")
        .layer("h{l+1}=h{l}@matrix(hidden, classes)+vector(classes)")
        .rememberAs("0")
        .constant("a", 0.9)
        .layerRepeat("h{l+1} = a*(dropout(A, 0.5)@h{l})+(1-a)*h{0}", 10)
        .classify();

ModelTraining trainer = new ModelTraining()
        .setOptimizer(new Adam(0.01))
        .setEpochs(300)
        .setPatience(100)
        .setLoss(new CategoricalCrossEntropy())
        .setVerbose(true)
        .setValidationLoss(new CategoricalCrossEntropy());

Model model = modelBuilder.getModel()
        .init(new XavierNormal())
        .train(trainer,
            nodes.samplesAsFeatures(), 
            nodeLabels, 
            nodes.range(0, 0.6), 
            nodes.range(0.6, 0.8));

Matrix output = model.predict(nodes.samplesAsFeatures()).get(0).cast(Matrix.class);
double acc = 0;
for(Long node : nodes.range(0.8, 1)) {
    Matrix trueLabels = dataset.labels().accessRow(node).asRow();
    Tensor nodeOutput = output.accessRow(node).asRow();
    acc += nodeOutput.argmax()==trueLabels.argmax()?1:0;
}
System.out.println("Acc\t "+acc/nodes.range(0.8, 1).size());

:thumbsup: Contributing

Feel free to contribute in any way, for example through the issue tracker. Please check out the contribution guidelines when bringing modifications to the code base.

:notebook: Citation

@article{krasanakis2023101459,
    title = {JGNN: Graph Neural Networks on native Java},
    journal = {SoftwareX},
    volume = {23},
    pages = {101459},
    year = {2023},
    issn = {2352-7110},
    doi = {https://doi.org/10.1016/j.softx.2023.101459},
    url = {https://www.sciencedirect.com/science/article/pii/S2352711023001553},
    author = {Emmanouil Krasanakis and Symeon Papadopoulos and Ioannis Kompatsiaris}
}