# DyNetx Tutorial¶

DyNetx is built upon networkx and is designed to configure, model and analyze dynamic networks.

In this tutorial we will introduce the `DynGraph`

object that can be used to describe undirected, temporal graphs.

## Creating a graph¶

Create an empty dynamic graph with no nodes and no edges.

```
import dynetx as dn
g = dn.DynGraph(edge_removal=True)
```

During the construction phase the `edge_removal`

parameter allows to specify if the dynamic graph will allow edge removal or not.

### Interactions¶

`G`

can be grown by adding one interaction at a time.
Every interaction is univocally defined by its endpoints, `u`

and `v`

, as well as its timestamp `t`

.

```
g.add_interaction(u=1, v=2, t=0)
```

Moreover, also interaction duration can be specified at creation time, by setting kwarg `e`

equal to the last timestamp at which the interaction is present:

```
g.add_interaction(u=1, v=2, t=0, e=3)
```

In the above example the interaction `(1, 2)`

appear at time `0`

and vanish at time `3`

, thus being present in `[0, 2]`

.

Interaction list can also be added: in such scenario all the interactions in the list will have a same timestamp (i.e. they will belong to a same network *snapshot*)

```
g.add_interactions_from([(1, 2), (2, 3), (3, 1)], t=2)
```

The same method can be used to add any `ebunch`

of interaction. An *ebunch* is any iterable container of interaction-tuples.

```
g.add_interactions_from(H.edges(), t=2)
```

### Nodes¶

Flattened node degree can be computed via the usual `degree`

method exposed by `networkx`

graph objects.
In order to get the time dependent degree a parameter `t`

, identifying the desired snapshot, must be specified.

Similarly, the `neighbors`

method has been extended with a similar optional filtering parameter `t`

.

## Read graph from file¶

`DyNetx`

allows to read/write networks from files in two formats:

snapshot graph (extended edgelist)

interaction graph (extended edgelist)

The former format describes the dynamic graph one edge per row as a 3-tuple

```
n1 n2 t1
```

where

`n1`

and`n2`

are nodes

`t1`

is the timestamp of interaction appearance

The latter format describes the dynamic graph one interaction per row as a 4-tuple

```
n1 n2 op t1
```

where

`n1`

and`n2`

are nodes

`t1`

is the timestamp of interaction appearance

`op`

identify either the insertion,`+`

, or deletion,`-`

of the edge

### Snapshot Graph¶

In order to read a snapshot graph file

```
g = dn.read_snapshots(graph_filename, nodetype=int, timestamptype=int)
```

in order to save a graph in the same format

```
dn.write_snapshots(graph, graph_filename)
```

### Interaction Graph¶

In order to read an interaction graph file

```
g = dn.read_interactions(graph_filename, nodetype=int, timestamptype=int)
```

in order to save a graph in the same format

```
dn.write_interactions(graph, graph_filename)
```

## Snapshots and Interactions¶

The timestamps associated to graph edges can be retrieved through

```
g.temporal_snapshots_ids()
```

Similarly, the number of interactions in a given snapshot can be obtained via

```
g.number_of_interactions(t=snapshot_id)
```

if the parameter `t`

is not specified a dictionary snapshot->edges number will be returned.

## Slicing a Dynamic Network¶

Once loaded a graph it is possible to extract from it a time slice, i.e., a time-span graph

```
s = g.time_slice(t_from=2, t_to=3)
```

the resulting `DynGraph`

stored in `s`

will be composed by nodes and interactions existing within the time span `[2, 3]`

.

## Obtain the Interaction Stream¶

A dynamic network can be also described as stream of interactions, a chronologically ordered list of interactions

```
for e in g.stream_interactions():
print e
```

the `stream_interactions`

method returns a generator that streams the interactions in `g`

, where `e`

is a 4-tuple `(u, v, op, t)`

`u, v`

are nodes

`op`

is a edge creation or deletion event (respectively`+`

,`-`

)

`t`

is the interactions timestamp