# IntroductionΒΆ

`pyunicorn`

(**Uni**fied **Co**mplex Network and **R**ecurre**N**ce
analysis toolbox) is a fully object-oriented Python package for the advanced
analysis and modeling of complex networks. Above the standard measures of
complex network theory such as degree, betweenness and clustering coefficient
it provides some **uncommon but interesting statistics** like Newman’s random
walk betweenness. `pyunicorn`

features novel **node-weighted (node splitting
invariant)** network statistics as well as measures designed for analyzing
**networks of interacting/interdependent networks**.

Moreover, `pyunicorn`

allows to easily **construct networks from uni- and
multivariate time series and event data** (functional (climate) networks and
recurrence networks). This involves linear and nonlinear measures of time
series analysis for constructing functional networks from multivariate data
(e.g. Pearson correlation, mutual information, event synchronization and event
coincidence analysis). `pyunicorn`

also features modern techniques of
nonlinear analysis of single and pairs of time series such as recurrence
quantification analysis (RQA), recurrence network analysis and visibility
graphs.

For example, to generate a recurrence network with 1000 nodes from a sinusoidal signal and compute its network transitivity you simply need to type

```
import numpy as np
from pyunicorn.timeseries import RecurrenceNetwork
x = np.sin(np.linspace(0, 10 * np.pi, 1000))
net = RecurrenceNetwork(x, recurrence_rate=0.05)
print(net.transitivity())
```

The package provides special tools to analyze and model **spatially embedded**
complex networks.

`pyunicorn`

is **fast** because all costly computations are performed in
compiled C, C++ and Fortran code. It can handle **large networks** through the
use of sparse data structures. The package can be used interactively, from any
Python script and even for parallel computations on large cluster
architectures.