SpSparse is quite different from Eigen::Sparse in a number of ways, and therefore interesting:
1. I would think of spsparse::Array vs. Eigen::Sparse in the same vein as Eigen::Tensor vs. Eigen::Matrix. spsparse::Array is desirable for the same reason as Eigen::Matrix.
2. Internally, spsparse::Array is (currently) a coordinate tensor: i.e., internally it has one array per index, plus an array for values. If nothing else, this is a convenient way to assemble the required triplets that can then be used as a constructor for Eigen::Matrix or Eigen::Vector. SpSparse is also good at consolidating duplicates in your original list.
3. Algorithms are written on SpSparse arrays by iterating through the elements. Algorithms are implemented on multiple arrays together by sorting and joining the elements. This is efficient for many operations, for example sparse-sparse tensor multiplication. You cannot directly ask a SpSparse array for the (i,j) element.
4. SpSparse makes no effort to offer genera linear algebra. Algorithms are focused on tensor representation, transformation and multiplication. Believe it or not, my application requires lots of sparse tensors and tensor multiplication, and no "more sophisticated" linear algebra algorithms. I think the reasonable thing to do would be to convert a SpSparse array to an Eigen::Matrix if one wishes to do more linear algebra on it.
5. SpSparse has the capability of supporting multiple storage formats underneath: variable-length std::vector, fixed-length through blitz::Array (would be ported to Eigen::Tensor). Also a single array of triplets (or their rank-n analog) is also a possible format. Storage format is abstracted away by the iterator interface, allowing algorithms to be written for these multiple use cases. The Eigen::Tensor underlying is particularly useful because you can make a spsparse::Array out of existing blocks of memory (for example, stuff you read out of a netCDF file). [Disclaimer: I've only implemented one underlying representation. The design is for multiple, and a prototype library did just that. Adding more storage formats will be easy when desired.]
6. CSR and CSC formats are also possible as storage formats.
7. Looking at Eigen's sparse matrix multiplication source code, my guess is that SpSparse does this faster. SpSparse multiples sparse matrices by sorting LHS row-major and RHS column-major, and then multiplying each row by each column. Each row-column multiplication is accomplished with a "join" iterator that scans through both sides in order, finding places where indices match. This is a direct analog to dense matrix multiplication. SpSparse multiplication would be faster because it has less nested looping. I realize that the required sorting might not be desirable or possible for all use cases.
SpSparse join iterators can also be used to do many operations at once. I was able to make a single-pass matrix multiplication that computes:
C * diag(S1) * A * diag(S2) * B * diag(S3)
...where C is a scalar, S1,S2,S3 are (optional) spsparse::Array<..., 1> and A and B are spsparse::Array<..., 2>. If this is the kind of product you need to compute, I doubt there is a faster way to do it.
7. SpSparse can also multiply sparse matrices (rank 2 tensors) by sparse vectors (rank 1 tensors) --- something I needed that Eigen did not provide in a direct way. More complex tensor multiplication would also be possible.
8. I've found that rank-1 SpSpare arrays are useful for many things I never thought of before.
For all these reasons, I think that SpSparse might make a nice addition to Eigen, probably renamed to Eigen::SparseTensor or something.