pyg_lib.partition
- metis(rowptr: Tensor, col: Tensor, num_partitions: int, node_weight: Optional[Tensor] = None, edge_weight: Optional[Tensor] = None, recursive: bool = False) Tensor [source]
Clusters/partitions a graph into multiple partitions via
METIS
, as motivated by the “Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks” paper.- Parameters:
rowptr (torch.Tensor) – Compressed source node indices.
col (torch.Tensor) – Target node indices.
num_partitions (int) – The number of partitions.
node_weight (torch.Tensor, optional) – Optional node weights. (default:
None
)edge_weight (torch.Tensor, optional) – Optional edge weights. (default:
None
)recursive (bool, optional) – If set to
True
, will use multilevel recursive bisection instead of multilevel k-way partitioning. (default:False
)
- Returns:
A vector that assings each node to a partition.
- Return type: