arbenson / higher-order-organization-julia Goto Github PK

When I try to run
G = max(A, A'); (undirected version of graph)
in jupyter notebook with Julia kernel, I get this error.

I am using the following packages:
using MatrixNetworks
using MAT
using PyPlot
using PyCall
data = matread("data/neuronal.mat");
A = data["A"];

It works fine will A, but when I try to run after that it (G = max(A,A') ) throws the below error.

MethodError: no method matching isless(::LinearAlgebra.Adjoint{Float64, SparseArrays.SparseMatrixCSC{Float64, Int64}}, ::SparseArrays.SparseMatrixCSC{Float64, Int64})
Closest candidates are:
isless(::Any, ::Missing) at missing.jl:88
isless(::Missing, ::Any) at missing.jl:87
isless(::Any, ::PyObject) at C:\Users\Manish.julia\packages\PyCall\ygXW2\src\pyoperators.jl:76
...

Stacktrace:
[1] max(x::SparseArrays.SparseMatrixCSC{Float64, Int64}, y::LinearAlgebra.Adjoint{Float64, SparseArrays.SparseMatrixCSC{Float64, Int64}})
@ Base .\operators.jl:480
[2] top-level scope
@ In[19]:1
[3] eval
@ .\boot.jl:368 [inlined]
[4] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
@ Base .\loading.jl:1428

Is it because of the certain versions of the packages. I don't know about the versions so if possible can you let me know what versions of the packages above have you used then?

Hi,

Thank you for your paper, it is the inspiration for my recent work and I have been able to use the motif conductance idea to improve some graph-related ML task. Currently I am looking for a general efficient algorithm to construct the motif co-occurrence matrix. I wonder if there is a software package or a principled approach asides from the brute-force algorithm? If you have time, could you please give me some pointers? Thank you very much in advance.

The correct URL appears to be juliabox.com.