pysurprise 1.0.1

Python bindings for the Surprise network community structure metric

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 project.

Installation

pip install pysurprise

To use the clustering algorithms that depend on optional packages:

# Install all algorithm dependencies
pip install pysurprise[algorithms]

# Or individually
pip install pysurprise[infomap]   # for Infomap
pip install pysurprise[igraph]    # for RN

From source

git clone https://github.com/raldecoa/SurpriseMe.git
cd SurpriseMe
pip install .

Quick start

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

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):

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:

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 project by Rodrigo Aldecoa and Ignacio Marín.

License

GPL-3.0-or-later. See LICENSE.