mlx_graphs.datasets.DBLP

class mlx_graphs.datasets.DBLP(base_dir: str | None = None, transform: Callable | None = None, pre_transform: Callable | None = None)

A subset of the DBLP computer science bibliography website, as collected in the “MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding” paper. DBLP is a heterogeneous graph containing four types of entities - authors (4,057 nodes), papers (14,328 nodes), terms (7,723 nodes), and conferences (20 nodes). The authors are divided into four research areas (database, data mining, artificial intelligence, information retrieval). Each author is described by a bag-of-words representation of their paper keywords.

Parameters:

• base_dir (Optional[str]) – directory where the dataset should be saved.

• transform (Optional[Callable]) – A function/transform that takes in an HeteroGraphData object and returns a transformed version. The data object will be transformed before every access. (default: None)

• pre_transform (Optional[Callable]) – A function/transform that takes in an HeteroGraphData object and returns a transformed version. The data object will be transformed before being saved to disk. (default: None)

__init__(base_dir: str | None = None, transform: Callable | None = None, pre_transform: Callable | None = None)

Methods

__init__([base_dir, transform, pre_transform])
download()	Download the dataset at self.raw_path.
load()	Load the processed dataset
process()	Process the dataset and store data in self.data
save()	Save the processed dataset

Attributes

name	Name of the dataset
num_edge_classes	Returns a dictionary of the number of edge classes for each edge type.
num_edge_features	Returns a dictionary of the number of edge features for each edge type.
num_edges	Returns a dictionary of the number of edges for each edge type.
num_graph_features	Returns the number of graph features.
num_items	Returns the number of items in the dataset.
num_node_classes	Returns a dictionary of the number of node classes for each node type.
num_node_features	Returns a dictionary of the number of node features for each node type.
num_nodes	Returns a dictionary of the number of nodes for each node type.
processed_path	The path where processed files are stored.
raw_file_names
raw_path	The path where raw files are stored.