EvolvingGraphs
Dynamic Graph Analysis Framework in Julia.
Installation:
Pkg.add("EvolvingGraphs")
Examples
We can generate the above evolving graph as
g = evolving_graph(Int, AbstractString)
add_edge!(g, 1, 2, "t1")
add_edge!(g, 1, 3, "t2")
add_edge!(g, 4, 5, "t2")
add_edge!(g, 2, 3, "t3")
add_edge!(g, 5, 6, "t3")Now g is a directed evolving graph with
6 nodes, 5 edges and 3 timestamps.
julia> g
Directed EvolvingGraph (6 nodes, 5 edges, 3 timestamps)We can find the weakly connected components and the shortest
temporal path of g
julia> weak_connected_components(g)
2-element Array{Array{Tuple,1},1}:
Tuple[(1,"t1"),(2,"t1"),(1,"t2"),(2,"t3"),(3,"t2"),(3,"t3")]
Tuple[(4,"t2"),(5,"t2"),(5,"t3"),(6,"t3")]
julia> shortest_temporal_path(g, (1, "t1"), (3, "t3"))
Temporal Path (3 walks) (1,"t1")->(1,"t2")->(3,"t2")->(3,"t3")We can also convert g to a list of adjacency matrices
julia> matrix(g, "t2")
6x6 Array{Bool,2}:
false false false false true false
false false false true false false
false false false false false false
false false false false false false
false false false false false false
false false false false false false
julia> spmatrix(g, "t2")
6x6 sparse matrix with 2 Bool entries:
[2, 4] = true
[1, 5] = truewhere the (i,j) entry is true if there is an edge from
nodes(g)[i] to nodes(g)[j] and false otherwise. Here we have
julia> nodes(g)[2]
4
julia> nodes(g)[4]
5
julia> nodes(g)[1]
1
julia> nodes(g)[5]
3and many more things.
