Dynamic Graph Forecasting in Java with JGraphT

Dynamic Graph Forecasting in Java with JGraphT

Below is a high‐level blueprint showing how you can implement a recurrent snapshot model entirely in Java using JGraphT. We’ll define interfaces for graph convolution, a GRU‐style updater, and an adjacency decoder—then show how they fit together in a pipeline that reads a list of graph snapshots and predicts the next snapshot.

---

1. Code Structure Overview

• GraphSnapshot: holds a JGraphT graph plus node feature map
• GraphConvLayer: interface for one layer of graph convolution
• GRUCell: updates per‐node hidden states over time
• EdgeDecoder: predicts edge probabilities from hidden states
• GraphForecaster: orchestrates snapshot embedding, recurrence, decoding

---

2. Key Interfaces and Classes

// A single snapshot of the graph
public class GraphSnapshot {
    Graph<Integer, DefaultEdge> graph;
    Map<Integer, double[]> nodeFeatures;
}

// One graph convolution layer
public interface GraphConvLayer {
    Map<Integer, double[]> apply(
        Graph<Integer, DefaultEdge> g,
        Map<Integer, double[]> features
    );
}

// A simple GRU cell for per‐node recurrence
public class GRUCell {
    private int dim;
    private double[][] Wz, Wr, Wh, Uz, Ur, Uh;
    private double[] bz, br, bh;

    public GRUCell(int dim) { /* initialize weights & biases */ }

    public Map<Integer, double[]> forward(
        Map<Integer, double[]> prevHidden,
        Map<Integer, double[]> inputFeatures
    ) {
        // implement z = sigmoid(Wz x + Uz h + bz), etc.
        // return new hidden map
    }
}

// Decode pairwise hidden states into edge probabilities
public class EdgeDecoder {
    private int dim;
    private double[][] W1, W2;
    private double[] b;

    public EdgeDecoder(int dim) { /* init */ }

    public double score(double[] h_i, double[] h_j) {
        // e.g. sigmoid( wᵀ [h_i; h_j] + b )
    }
}

---

3. Putting It All Together: GraphForecaster

public class GraphForecaster {
    private GraphConvLayer conv;
    private GRUCell gru;
    private EdgeDecoder decoder;
    private double threshold = 0.5;

    public GraphForecaster(
        GraphConvLayer conv,
        GRUCell gru,
        EdgeDecoder decoder
    ) {
        this.conv = conv;
        this.gru = gru;
        this.decoder = decoder;
    }

    public Graph<Integer, DefaultEdge> predictNext(
        List<GraphSnapshot> history
    ) {
        Map<Integer, double[]> hidden = new HashMap<>();

        // 1. Iterate through snapshots
        for (GraphSnapshot snap : history) {
            // a) Graph convolution
            Map<Integer, double[]> features = conv.apply(snap.graph, snap.nodeFeatures);

            // b) Recurrent update
            hidden = gru.forward(hidden, features);
        }

        // 2. Build predicted graph
        Graph<Integer, DefaultEdge> nextGraph = new DefaultUndirectedGraph<>(DefaultEdge.class);

        // assume same node set as last snapshot
        Set<Integer> nodes = history.get(history.size() - 1).graph.vertexSet();
        nodes.forEach(nextGraph::addVertex);

        // 3. Decode edges
        for (Integer u : nodes) {
            for (Integer v : nodes) {
                if (u >= v) continue;  // avoid duplicates
                double p = decoder.score(hidden.get(u), hidden.get(v));
                if (p > threshold) {
                    nextGraph.addEdge(u, v);
                }
            }
        }

        return nextGraph;
    }
}

---

4. Algorithmic Details

• GraphConvLayer:

  • For each node i, compute
    fᵢ′ = W_self·fᵢ + ∑_{j∈N(i)} W_nb·fⱼ, optionally normalized by degree.

• GRUCell:

  • Standard update equations
    z = σ(W_z·x + U_z·h + b_z)
    r = σ(W_r·x + U_r·h + b_r)
    h̃ = tanh(W_h·x + U_h·(r⊙h) + b_h)
    h' = z⊙h + (1−z)⊙h̃

• EdgeDecoder:

  • Concatenate hᵢ and hⱼ into a vector of size 2·dim
  • Apply a linear layer followed by sigmoid to get probability

---

5. Next Steps

• Implement batching of snapshots if graphs are large
• Tune threshold or learn it as a parameter
• Extend to directed or weighted graphs by adjusting decoder
• Experiment with attention‐based temporal modules instead of GRU

With this Java/JGraphT skeleton, you can swap in any convolution or recurrence algorithm that fits your transformation needs. From here, you’re free to optimize, parallelize, or integrate custom scheduling for very large temporal networks.