Source code for graphnetz.datasets.finance

"""Finance and economics networks.

Coverage:

- Trade: ``product_space`` (economic complexity).
- Ownership / corporate control: ``board_directors`` (Norwegian boards).
- Innovation: ``us_patents`` citation network.
- Transactions / fraud / AML: PyG ``EllipticBitcoinDataset`` (illicit-wallet
  detection on Bitcoin transactions).
- Open Graph Benchmark (optional ``ogb`` extra): ``ogbn_products``
  (Amazon co-purchase graph for node classification, ~2.4 M nodes,
  47 product categories).

Inter-bank exposure datasets are typically confidential and have no canonical
public benchmark.
"""

from torch_geometric.data import Data
from torch_geometric.datasets import EllipticBitcoinDataset

from graphnetz.datasets._netz import Netz
from graphnetz.datasets._ogb import load_ogb_node




def product_space(root: str) -> Netz:
    """Product space of international trade (economic complexity)."""
    return Netz(root=root, dataset_name="product_space", network_name="product_space")





def board_directors(root: str, network_name: str = "net1m_2002-05-01") -> Netz:
    """Norwegian boards of directors interlock network (snapshot)."""
    return Netz(root=root, dataset_name="board_directors", network_name=network_name)





def us_patents(root: str) -> Netz:
    """US patents citation network."""
    return Netz(root=root, dataset_name="us_patents", network_name="us_patents")





def elliptic_bitcoin(root: str) -> EllipticBitcoinDataset:
    """Elliptic Bitcoin transactions dataset for illicit-wallet detection."""
    return EllipticBitcoinDataset(root=root)





def ogbn_products(root: str) -> Data:
    """OGB Amazon product co-purchase network (~2.4 M nodes, 47 classes).

    Larger than ``ogbn_arxiv`` — full-graph training is feasible on a
    workstation GPU but slow; reduce ``epochs`` for quick iteration.
    """
    return load_ogb_node("ogbn-products", root)



__all__ = ["board_directors", "elliptic_bitcoin", "ogbn_products", "product_space", "us_patents"]