API reference

Auto-generated reference for the public surface of GraphNetz. The reference is organised by module so deep links stay stable as the package grows; the top-level convenience imports below cover everything you’ll typically need.

• graphnetz.datasets
• graphnetz.models
• graphnetz.benchmark
• graphnetz.training
• graphnetz.plotting

Top-level convenience imports

Importable directly from graphnetz:

Models

GCN	Two-layer Graph Convolutional Network.
GAT	Two-layer Graph Attention Network.
GIN	Graph Isomorphism Network for graph-level prediction.
GraphSAGE	Two-layer GraphSAGE for node-level prediction.
GraphTransformer	Two-layer graph transformer based on TransformerConv.
DGI	Deep Graph Infomax for unsupervised node representation learning.

Datasets

CATEGORIES	Mapping of category name to the dataset module that implements it.
Netz	Netzschleuder network dataset.
download_all_networks_netz	Download and process every network in a Netzschleuder dataset.
list_datasets	Return loader names organized by category and task.

Benchmark

BENCHMARK_TASKS	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
BenchmarkReport	Structured outcome of a multi-seed benchmark run.
ModelSpec	How to instantiate a model and which task tasks it supports.
Task	A single benchmark task_type: a dataset loader plus its training task.
iter_benchmark_tasks	Flatten BENCHMARK_TASKS to a list, optionally filtered by category/task.
plot_benchmark	Grouped bar chart with mean ± CI error bars.
register_model	Register a model with the benchmark dispatcher.
register_task	Register task under category in BENCHMARK_TASKS.
run_benchmark	Run a benchmark across one or more (model, task, seed) combinations.
task_from_dataset	Wrap an already-loaded dataset as a Task.
unregister_task	Remove a previously registered task; returns it, or None if absent.

Training utilities

train_node_classification	Train a node classifier with Planetoid-style train/val/test masks.
train_graph_classification	Train a graph-level classifier.
train_graph_regression	Train a graph-level regressor (MSE loss, MAE on val).
train_link_prediction	Train a link predictor with binary cross-entropy on RandomLinkSplit.
train_dgi	Train a Deep Graph Infomax model (unsupervised).
train_node_degree_regression	Self-supervised node-level regression: predict log node degree.
train_relational_link_prediction	Train a relational link predictor (DistMult) on knowledge graph triples.

Plotting

figure	Create a sized figure.
panel_label	Add a bold panel label (a, b, ...) to an axis.
plot_grouped_bars	Grouped bar chart from a {group: {series: value}} mapping.
plot_history	Plot a training history dict.
save_figure	Save fig to one path stem in multiple formats; returns the saved paths.
set_plot_style	Apply the rcParams and color cycle.