MatrixNetworks.jl
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JuliaGraphs
Graph and Network algorithms in Julia
mst_prim_matrix fails if you have real-valued data with an inexact error on line 194. This is because tv has a type int64, whereas ai could be floating point valued.
If you run @show scomponents(A) then it outputs ALL of the information with no truncation. This breaks IJulia if you have a large matrix.
The Laplacian energy is an interesting function of a graph -- doi:10.1016/j.laa.2005.09.008 Laplacian Energy LE(G) = sum |lambda_i - avg-deg| Threshold graphs maximize Laplacian Energy.
It'd be nice to have a few matrix-based network models such as Erdos-Renyi, Weighted Erdos-Renyi, Kronecker, etc. As well as the growth models like forest-fire, preferential attachment, etc.
One of the issues in matrix equilibration/normalization/etc is the structural rank of a matrix. This asks if there is an assignment of values to the non-zero elements such that the...