Examples for Heterogeneous Data

Example	Description
`hetero_conv_dblp.py`	Shows how to use the `HeteroConv(...)` wrapper; Trains it for node classification on the `DBLP` dataset.
`to_hetero_mag.py`	Shows how to use the `to_hetero(...)` functionality; Trains it for node classification on the `ogb-mag` dataset.
`hetero_link_pred.py`	Shows how to use the `to_hetero(...)` functionality; Trains it for link prediction on the `MovieLens` dataset.
`hgt_dblp.py`	Trains a Heterogeneous Graph Transformer (HGT) model for node classification on the `DBLP` dataset.
`hierarchical_sage.py`	Shows how to perform hierarchical sampling; Trains a heterogeneous `GraphSAGE` model for node classification on the `ogb-mag` dataset.
`load_csv.py`	Shows how to create heterogeneous graphs from raw `*.csv` data.
`metapath2vec.py`	Train an unsupervised `MetaPath2Vec` model; Tests embeddings for node classification on the `AMiner` dataset.
`temporal_link_pred.py`	Trains a heterogeneous `GraphSAGE` model for temporal link prediction on the `MovieLens` dataset.
`bipartite_sage.py`	Trains a GNN via metapaths for link prediction on the `MovieLens` dataset.
`bipartite_sage_unsup.py`	Trains a GNN via metapaths for link prediction on the large-scale `TaoBao` dataset.
`dmgi_unsup.py`	Shows how to learn embeddings on the `IMDB` dataset using the `DMGI` model.
`han_imdb.py`	Shows how to train a heterogeneous Graph Attention Network (HAN) for node classification on the `IMDB` dataset.
`recommender_system.py`	Shows how to train a temporal GNN-based recommender system on the `MovieLens` dataset.