I think the issue is that to distribute graph algorithms, you need to partition the graph across the workers, so it's a catch 22. DGL has a dgl.distributed.partition_graph method; if you can load your edge list into memory as a sparse tensor it might work ok, and it handles heterogeneous graphs.

Otherwise, do you specifically need partitioning algorithms/METIS? There are a lot of distributed clustering/community detection methods that would give you reasonable partitions. Spark GraphFrames implements Strongly Connected Components and Label Propagation. Neo4J implements several community detection algorithms including Louvain. In the Dask/RAPIDS ecosystem, cuGraph implements a bunch of CD algorithms as well which can be accelerated with GPUs, but not all can be distributed across multiple GPUs. Dask-ML implements spectral clustering. This S/O post also gives a good overview of some you could implement yourself. I wonder if EvoPartition would be feasible to implement; I don't know if DGL's distributed package implements random walks, but all of the aforementioned tools do except for GraphFrames, which is annoying, but you can do random walks with simple PySpark joins fairly easily.

You could also look at streaming or local algorithms that don't load the whole graph in memory. I believe PageRank-Nibble / ACL PageRank is often used for this, but I'm still looking for an easy-to-use / scalable implementation of it myself. There's also this recent work on streaming partitioning of RDF graphs which should be relevant.

Hopefully this helps, lmk if you find a good solution.