  Graph-based representations underlie a wide range of scientific problems.
  Graph connectivity is typically represented as a sparse matrix in the
Compressed Sparse Row format. Large-scale graphs rely on distributed storage,
allocating distinct subsets of rows to compute nodes.
  Efficient matrix transpose is an operation of high importance, providing the
reverse graph pathways and a column-ordered matrix view. This operation is well
studied for simple graph models. Nevertheless, its resolution for multigraphs
and higher-cardinality connectivity matrices is unexistent.
  We advance state-of-the-art distributed transposition methods by providing a
theoretical model, algorithmic details, MPI-based implementation and proof of
mathematical soundness for such complex models. Benchmark results demonstrate
ideal and almost ideal scaling properties for perfectly- and
heterogeneously-balanced datasets, respectively
