# EvolvingGraphs

Studying networks that evolve over time.

## Get Started

We model a time-dependent network, a.k.a an evolving graph, as a ordered sequence of static graphs such that each static graph represents the interaction between nodes at a specific time stamp. The figure below shows an evolving graph with 3 timestamps.

Using `EvolvingGraphs`

, we could simply construct this graph by using the function
`add_bunch_of_edges!`

, which adds a list of edges all together.

```
julia> using EvolvingGraphs
julia> g = EvolvingGraph()
Directed EvolvingGraph 0 nodes, 0 static edges, 0 timestamps
julia> add_bunch_of_edges!(g, [(1,2,1),(1,3,2),(2,3,3)])
Directed EvolvingGraph 3 nodes, 3 static edges, 3 timestamps
julia> edges(g)
3-element Array{EvolvingGraphs.WeightedTimeEdge{EvolvingGraphs.Node{Int64},Int64,Float64},1}:
Node(1)-1.0->Node(2) at time 1
Node(1)-1.0->Node(3) at time 2
Node(2)-1.0->Node(3) at time 3
```

Now you've created your first evolving graph! Congrats!

To learn more about evolving graphs and why they are useful, please have a look at our tutorial.

## References

Weijian Zhang,
"Dynamic Network Analysis in Julia",
*MIMS EPrint*, 2015.83, (2015).
[pdf]

Jiahao Chen and Weijian Zhang,
"The Right Way to Search Evolving Graphs",
*MIMS EPrint*, 2016.7, (2016)
[pdf]
[source]