Doubling the weights of self-edges about simpleweightedgraphs.jl

[sbromberger]

This is expected (if not explicitly intended) for undirected graphs, as the adjacency matrix of an undirected graph yields 2 for self-loops:

g = Graph(3)
add_edge!(g, 1, 1)
Matrix(adjacency_matrix(g))

gives

2 0 0
0 0 0
0 0 0

[pwl]

Sorry, I'm at loss as to how the adjacency matrix comes in here, I'm new to the graph terminology. How is the adjacency matrix related to the weights in this context?

Am I on the right track to think that SimpleWeightedGraph(srcs, dsts, weights) is conceptually equivalent to creating a directed graph first and then converting it to an undirected one by summing all the edges, where self edges are counted twice because their adjacency matrix entry is 2?

And a related question, what would be the recommended way to construct a weighted graph that I expected to get in the first place? That is SimpleWeightedGraph(srcs, dsts, weights) without doubling the self-edges? Currently I directly construct the sparse matrix and build a weighted graph with it.

[sbromberger]

SimpleWeightedGraphs are stored as a sparse matrix with non-zero weights denoting adjacencies. Self-loops are a bit of an afterthought and aren't guaranteed to actually work with all algorithms. However, what you can do is simply iterate over the vertices and divide the weights by two, readding the edges.

A PR would also be welcome, if for nothing else than discussion as to the best way to proceed.