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 (
Tensor
) – Compressed source node indices.col (
Tensor
) – Target node indices.num_partitions (
int
) – The number of partitions.node_weight (
Optional
[Tensor
], default:None
) – The node weights.edge_weight (
Optional
[Tensor
], default:None
) – The edge weights.recursive (
bool
, default:False
) – If set toTrue
, will use multilevel recursive bisection instead of multilevel k-way partitioning.
- Returns:
Tensor
– A vector that assings each node to a partition.