Sequential Graph Partition

How to perform sequential graph partition in distributed training? Or is there a way to manually partition the input graph and store the partition information to files, as dgl.distributed.partition_graph() do? The fact is I have a small input test graph, and I want to assign the nodes to partitions manually. It seems only random or Metis partition can be used currently.

Not supported. Sequentially adding nodes to existing partition involves several modules/parts such as graph.dgl, meta.json which records the node mapping. For now, both random and metis first obtain the node assignments via calls like, then generate partitions. You probably could look deep into this part and hack it on your own.

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I will take a deep look into this. Thanks a lot!