pysurprise 1.1.1
Python bindings for the Surprise network community structure metric
Requires Python: >=3.8
Keywords: networks, community-detection, surprise, graph, clustering, bioinformatics

# pySurprise

Python package for computing the **Surprise** metric and running community
detection algorithms on complex networks.

Surprise quantifies the quality of a partition of a complex network into
communities using a cumulative hypergeometric distribution. Higher values
indicate a more *surprising* (i.e. better) community structure.

**pySurprise** wraps the original C++ Surprise computation via pybind11 for
maximum performance, and provides Python access to **7 clustering algorithms**
from the [SurpriseMe](https://github.com/raldecoa/SurpriseMe) project.

## Installation

```bash
pip install pysurprise
```

To use the clustering algorithms that depend on optional packages:

```bash
# Install all algorithm dependencies
pip install pysurprise[algorithms]

# Or individually
pip install pysurprise[infomap]   # for Infomap
pip install pysurprise[igraph]    # for RN
```

### From source

```bash
git clone https://github.com/raldecoa/SurpriseMe.git
cd SurpriseMe
pip install .
```

## Quick start

```python
import pysurprise

# Define edges (undirected, listed once)
edges = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3),
         (3, 4), (4, 5), (4, 6), (4, 7), (5, 6), (5, 7), (6, 7)]

# Partition: node i belongs to community partition[i]
partition = [0, 0, 0, 0, 1, 1, 1, 1]

value = pysurprise.surprise(edges, partition)
print(f"Surprise = {value}")
```

### Using string labels

```python
edges = [("a", "b"), ("a", "c"), ("b", "c"),
         ("c", "d"), ("d", "e"), ("d", "f"), ("e", "f")]

partition = {"a": 0, "b": 0, "c": 0, "d": 1, "e": 1, "f": 1}

value = pysurprise.surprise(edges, partition)
```

### Low-level access

If you already have the four Surprise parameters (F, M, n, p):

```python
from pysurprise import compute_surprise

F = 28.0   # total possible pairs: N*(N-1)/2
M = 12.0   # intra-community possible pairs
n = 13.0   # total edges
p = 12.0   # intra-community edges

value = compute_surprise(F, M, n, p)
```

## Clustering algorithms

`pysurprise.algorithms` exposes the 7 community detection algorithms from
SurpriseMe, ready to use from Python:

```python
from pysurprise import algorithms
import pysurprise

# Run a single algorithm
partition = algorithms.cpm(edges)
score = pysurprise.surprise(edges, partition)

# Run all 7 algorithms and compare
results = algorithms.run_all(edges)
for name, part in results.items():
    s = pysurprise.surprise(edges, part)
    print(f"{name}: {s:.4f}")
```

| Algorithm | Function | Reference |
|-----------|----------|-----------|
| **CPM** | `algorithms.cpm()` | Traag et al., *Phys. Rev. E* 84 (2011) |
| **Infomap** | `algorithms.infomap()` | Rosvall & Bergstrom, *PNAS* 105 (2008) |
| **RB** | `algorithms.rb()` | Reichardt & Bornholdt, *Phys. Rev. E* 74 (2006) |
| **RN** | `algorithms.rn()` | Ronhovde & Nussinov, *Phys. Rev. E* 81 (2010) |
| **RNSC** | `algorithms.rnsc()` | King et al., *Bioinformatics* 20 (2004) |
| **SCluster** | `algorithms.scluster()` | Aldecoa & Marín, *PLoS ONE* 5 (2010) |
| **UVCluster** | `algorithms.uvcluster()` | Arnau et al., *Bioinformatics* 21 (2005) |

All functions accept `edges: list[(str, str)]` and return
`dict[str, int]` (node → community).

## API Reference

| Function | Description |
|---|---|
| `surprise(edges, partition)` | High-level: compute Surprise from edges and partition (list or dict) |
| `compute_surprise(F, M, n, p)` | Low-level: compute Surprise from the four parameters directly |
| `surprise_from_partition(edges, partition)` | C++ binding: int-indexed edges and partition list |
| `surprise_from_partition_dict(edges, partition)` | C++ binding: string-labelled edges and partition dict |
| `log_hyper_probability(F, M, n, j)` | Single hypergeometric probability term (log10) |
| `algorithms.run_all(edges)` | Run all 7 clustering algorithms and return results |

## Parameters

- **F**: Total number of possible node pairs = N×(N−1)/2
- **M**: Sum of intra-community possible pairs = Σ s_i×(s_i−1)/2
- **n**: Total number of edges in the network
- **p**: Number of intra-community edges

## Credits

**Developed by** José Marín.

**Based on the original Surprise metric and SurpriseMe software by:**

> Aldecoa R, Marín I (2011). *Deciphering network community structure by Surprise.*
> PLoS ONE 6(9): e24195.

> Aldecoa R, Marín I (2013). *Surprise maximization reveals the community structure
> of complex networks.* Scientific Reports 3, 1060.

The C++ Surprise computation and bundled clustering algorithms originate from
the [SurpriseMe](https://github.com/raldecoa/SurpriseMe) project by
Rodrigo Aldecoa and Ignacio Marín.

## License

GPL-3.0-or-later. See [LICENSE](LICENSE).