8.1. Sparsity and structure#

Very large matrices cannot be stored all within primary memory of a computer unless they are sparse. A sparse matrix has structural zeros, meaning entries that are known to be exactly zero.} For instance, the adjacency matrix of a graph has zeros where there are no links in the graph. To store and operate with a sparse matrix efficiently, it is not represented as an array of all of its values. There is a variety of sparse formats available; for the most part, you can imagine that the matrix is stored as triples $$(i,j,A_{ij})$$ for all the nonzero $$(i,j)$$ locations.

Computing with sparse matrices#

Most graphs with real applications have many fewer edges than the maximum possible $$n^2$$ for $$n$$ nodes. Accordingly, their adjacency matrices have mostly zero elements and should be represented sparsely. Julia functions to deal with sparse matrices are found in the SparseArrays package in the standard library.

Demo 8.1.1

Here we load the adjacency matrix of a graph with 2790 nodes. Each node is a web page referring to Roswell, NM, and the edges represent links between web pages. (Credit goes to Panayiotis Tsaparas and the University of Toronto for making this data public.)

@load "roswell.jld2" A;      # file is on the book's website

m,n = size(A)
@show density = nnz(A) / (m*n);

density = nnz(A) / (m * n) = 0.0010902994565845762


The computer memory consumed by any variable can be discovered using summarysize. We can use it to compare the space needed for the sparse representation to its dense counterpart, that is, the space needed to store all the elements, whether zero or not.

F = Matrix(A)
Base.summarysize(F) / Base.summarysize(A)

550.268980630567


As you can see, the storage savings are dramatic. Matrix-vector products are also much faster using the sparse form because operations with structural zeros are skipped.

x = randn(n)
A*x;   # make sure * is loaded and compiled
@elapsed for i in 1:300; A*x; end

0.003042209

F*x;
@elapsed for i in 1:300; F*x; end

0.650395333


Arithmetic operations such as +, -, *, and ^ respect and exploit sparsity if the matrix operands are sparse. However, matrix operations may substantially decrease the amount of sparsity, a phenomenon known as fill-in.

Demo 8.1.2

Here is the adjacency matrix of a graph representing a small-world network, featuring connections to neighbors and a small number of distant contacts.

@load "smallworld.jld2" A
graphplot(A,linealpha=0.5)


Because each node connects to relatively few others, the adjacency matrix is quite sparse.

spy(A,title="Nonzero locations",m=2,color=:blues)


By Theorem 7.1.5, the entries of $$\mathbf{A}^k$$ give the number of walks of length $$k$$ between pairs of nodes, as with “k degrees of separation” within a social network. As $$k$$ grows, the density of $$\mathbf{A}^k$$ also grows.

plt = plot(layout=(1,3),legend=:none,size=(600,240))
for k in 2:4
spy!(A^k,subplot=k-1,color=:blues,
title=latexstring("\\mathbf{A}^\$k"))
end
plt