relationalstats 0.1.0a1

Practical and statistical social network analysis tools for Python.

relationalstats

A practical Python toolkit for applied and statistical social network analysis, with research-grade documentation and transparent methodological boundaries.

relationalstats provides Python APIs for statistical network analysis workflows inspired by R sna, ergm, tergm / stergm, and linkprediction, while remaining explicit about what is equivalent, approximate, experimental, or planned.

What is included

Area	Status
Link prediction	Initial core implemented
QAPLogit	Initial core implemented
ERGM	Initial dyadic-logistic approximation
STERGM	Initial separable dyadic-logistic approximation
R validation	Placeholders and roadmap; fixtures pending

Installation for local development

python3.10 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev,test]"

Optional extras:

python -m pip install -e ".[docs]"
python -m pip install -e ".[notebooks,plot]"
python -m pip install -e ".[ml]"
python -m pip install -e ".[r]"

Quick usage

Link prediction

import networkx as nx
from relationalstats.linkprediction import ProxFun, proxfun_full

G = nx.path_graph(3)

scores = proxfun_full(
    G,
    pairs=[(0, 2)],
    metrics=["common_neighbors", "jaccard", "adamic_adar"],
)

result = ProxFun(metrics=["jaccard", "adamic_adar"]).fit(G)
result.to_dataframe()

QAPLogit

from relationalstats.datasets import make_qap_toy_data
from relationalstats.qap import QAPLogit

y, x_matrices = make_qap_toy_data()

result = QAPLogit(
    n_permutations=99,
    random_state=42,
    backend="statsmodels",
).fit(y, x_matrices)

result.to_dataframe()

ERGM approximation

from relationalstats.datasets import make_florentine_like_graph
from relationalstats.ergm import ERGM

G = make_florentine_like_graph()

result = ERGM(
    terms=["edges", "common_neighbors", "degree1", "gwesp", "nodematch:faction"],
    backend="sklearn",
    random_state=42,
).fit(G)

result.to_dataframe()
result.gof(n_sim=100, seed=123)

STERGM approximation

from relationalstats.datasets import make_stergm_temporal_toy
from relationalstats.stergm import STERGM

G1, G2 = make_stergm_temporal_toy()

result = STERGM(
    formation_terms=["edges", "common_neighbors", "degree1", "gwesp"],
    dissolution_terms=["edges", "common_neighbors", "degree1", "gwesp"],
    backend="sklearn",
    random_state=42,
).fit(G1, G2)

result.to_dataframe()
result.simulate(seed=123)

Documentation map

• Documentation index
• Link prediction
• QAP
• ERGM approximation
• STERGM approximation
• Equivalence vs approximation
• Reproducibility
• Roadmap

Examples

• Examples index
• Link prediction examples
• QAP examples
• ERGM examples
• STERGM examples

Run examples:

python examples/linkprediction/proxfun_feature_pipeline.py
python examples/qap/qap_logit_toy.py
python examples/qap/qap_logit_backend_comparison.py
python examples/ergm/ergm_florentine_like.py
python examples/stergm/stergm_temporal_toy.py

Repository structure

relationalstats/
├── docs/
│   ├── README.md
│   ├── ergm/
│   ├── linkprediction/
│   ├── methodology/
│   ├── qap/
│   └── stergm/
├── examples/
│   ├── README.md
│   ├── ergm/
│   ├── linkprediction/
│   ├── qap/
│   └── stergm/
├── notebooks/
│   └── README.md
├── scripts/
├── src/
│   └── relationalstats/
│       ├── datasets/
│       ├── ergm/
│       ├── linkprediction/
│       ├── modules/
│       ├── qap/
│       └── stergm/
├── tests/
│   ├── unit/
│   └── validation_against_r/
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE
├── pyproject.toml
└── README.md

Running tests

source .venv/bin/activate
pytest -q

Focused test groups:

pytest tests/unit/test_linkprediction.py tests/unit/test_linkprediction_manual_graphs.py -q
pytest tests/unit/test_qap.py tests/unit/test_qap_permutation.py -q
pytest tests/unit/test_ergm_statistics.py tests/unit/test_ergm_gof.py -q
pytest tests/unit/test_stergm.py -q

Build and distribution validation

source .venv/bin/activate
rm -rf dist build *.egg-info
python -m pip install build twine
python -m build
python -m twine check "dist/*"
ls -lh dist/

Release flow

feature/* -> develop -> release/vX.Y.Z -> main -> tag vX.Y.Z -> PyPI

Pre-release versions should follow PEP 440:

0.1.0a1
0.1.0b1
0.1.0rc1
0.1.0

Methodological transparency

Current ERGM and STERGM implementations are dyadic-logistic approximations, not full MCMC-MLE implementations equivalent to R ergm, tergm, or stergm.

Validation against R

Planned validation fixtures include:

• sna::netlogit for QAP-style logistic models;
• linkprediction::proxfun for link prediction metrics;
• ergm and network for selected network statistics;
• tergm / stergm for temporal dyad construction where feasible.

Academic and private material policy

Solved academic notebooks, private experiments, raw course material, and non-public validation files should not be committed to the public repository.

Author

• Hubert Ronald - Initial Work - HubertRonald

License

The source code in this repository is distributed under the MIT License. See LICENSE for more details.