Dynamic Graph Analysis Framework in Julia.

Installation:

`Pkg.add("EvolvingGraphs")`

We can generate the above evolving graph as

```
julia> i = [1, 1, 4, 2, 5];
julia> j = [2, 3, 5, 3, 6];
julia> t = ["t1", "t2", "t2", "t3", "t3"];
julia> g = evolving_graph(i,j,t)
```

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 shortest temporal path of `g`

by

```
help?> shortest_temporal_path
shortest_temporal_path(g, v1, t1, v2, t2 [, verbose = false])
Find the shortest temporal path from node v1 at timestamp t1 to node v2 at
timestamp t2 on the evolving graph g. If verbose = true, prints the current
path at each search step.
julia> shortest_temporal_path(g, 1, "t1", 3, "t3")
(Node(1),"t1")->(Node(1),"t2")->(Node(3),"t2")->(Node(3),"t3")
```

We can also convert `g`

to a list of adjacency matrices

```
julia> matrix(g, "t2")
6x6 Array{Float64,2}:
0.0 0.0 1.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
julia> spmatrix(g, "t2")
6x6 sparse matrix with 2 Float64 entries:
[1, 3] = 1.0
[4, 5] = 1.0
```

02/01/2015

7 days ago

368 commits