This release is a pre-release and may not be stable for production use.
asbm
Infer network groups and properties with assortative stochastic block models
Maximilian Jerdee
This Python package uses Bayesian inference to find meaningful groupings of nodes in networks.
We implement a general assortative SBM that unifies the standard SBM and the planted partition model under a common framework. Its parameters directly measure the violation of each model's assumptions: the assortative preference ρ_in/ρ_out captures departure from in/out symmetry, while per-group variation coefficients v_in and v_out measure heterogeneity across groups. The standard SBM, planted partition model, and the Zhang–Peixoto hybrid emerge as special cases, enabling exact Bayesian model comparison between them.
For each model, the package includes algorithms to:
- Find consensus estimates of the group structure
- Infer global network parameters (assortativity, group sizes)
- Score held-out edges via posterior predictive likelihood
Installation
Implementations are available for Python, R, and Julia.
Python
pip install asbm
Or build locally from the repository root:
pip install .
R
install.packages("asbm", repos = "https://maxjerdee.r-universe.dev")
Julia
using Pkg
Pkg.add(url="https://github.com/maxjerdee/asbm", subdir="bindings/julia")
Building from source requires CMake and a C++17 compiler. Run Pkg.build("ASBM") after installation to compile the native library.
Quickstart
Python
import asbm
import networkx as nx
G = nx.read_gml("examples/data/dolphins.gml", label="id")
config = asbm.Config(
model="general_asbm",
degree_correction=True,
)
result = asbm.fit(config, G)
print(result.mdl_partition)
print(result.mdl_value)
print(result.consensus_partition())
To score held-out edges:
G_train = nx.read_gml("examples/data/train.gml")
G_test = nx.read_gml("examples/data/test.gml")
result = asbm.fit(asbm.Config(model="general_asbm"), G_train)
score = result.log_posterior_predictive(G_test)
R
library(asbm)
library(igraph)
G <- read_graph("examples/data/dolphins.gml", format = "gml")
result <- fit(G,
model = "general_asbm",
degree_correction = TRUE,
num_chains = 4,
samples_per_chain = 100,
seed = 42)
print(result$mdl_value)
print(result$mdl_partition)
print(result$consensus_partition)
Julia
using ASBM, Graphs
g = cycle_graph(62) # or load via GraphIO
result = fit(g;
model = "general_asbm",
degree_correction = true,
num_chains = 4,
samples_per_chain = 100,
seed = 42)
println(result.mdl_value)
println(result.mdl_partition)
println(result.consensus_partition)
For the full API, including posterior predictive evaluation and the samples schema, see the package documentation.
1 maintainer
Metadata
1 maintainer
Release files for asbm 0.2.1.dev3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| asbm-0.2.1.dev3.tar.gz | 230.5 kB | Details |
Built distributions (wheels)
Total release size: 7.6 MB
Release files / asbm-0.2.1.dev3.tar.gz
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