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name: graph-time-series-workflows description: "Guides StellarGraph graph-level classification and spatio-temporal forecasting with PaddedGraphGenerator, GCN graph classifiers, DeepGraphCNN, SortPooling, SlidingFeaturesNodeGenerator, and GCN_LSTM." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Graph Classification and Time Series

Use this sub-skill for graph-level supervised learning or spatio-temporal forecasting where the unit of prediction is a whole graph or a future sequence on graph nodes.

Read first

• references/graph-classification.md for PaddedGraphGenerator, graph-level labels, GCNSupervisedGraphClassification, DeepGraphCNN, and SortPooling.
• references/time-series.md for SlidingFeaturesNodeGenerator, fixed adjacency, GCN_LSTM, window sizes, and forecast target shapes.
• references/troubleshooting.md for padded graph, graph-label, adjacency, sequence, and target-distance shape failures.
• scripts/graph_batch_smoke.py and scripts/time_series_shape_smoke.py for safe tiny shape checks without downloads.

Route here when the user asks to

• classify a collection of graphs with graph labels;
• use PaddedGraphGenerator, GCNSupervisedGraphClassification, DeepGraphCNN, or SortPooling;
• forecast traffic/sensor/time-series values over graph nodes;
• use SlidingFeaturesNodeGenerator, FixedAdjacencyGraphConvolution, or GCN_LSTM;
• debug graph batch padding, graph-level targets, fixed adjacency, seq_len, or sliding-window target distance.

Route elsewhere

• Node classification on one graph: ../node-classification-gnns/SKILL.md.
• Link prediction: ../link-prediction-kg/SKILL.md.
• Raw graph construction: ../graph-data-loading/SKILL.md.
• Generator mechanics: ../sampling-generators/SKILL.md.

Operating workflow

1. Decide whether the sample unit is a graph (PaddedGraphGenerator) or a time window over nodes (SlidingFeaturesNodeGenerator).
2. For graph classification, create a list of StellarGraph objects and a target vector/DataFrame with one row per graph.
3. For time series, create a graph with sequence-valued node features and a fixed adjacency matrix compatible with GCN_LSTM.
4. Inspect one generator batch before adding model layers.
5. Add Keras output heads/losses for graph-level classification/regression or forecasting targets.

Safe checks

python sub-skills/graph-time-series-workflows/scripts/graph_batch_smoke.py
python sub-skills/graph-time-series-workflows/scripts/time_series_shape_smoke.py