NetworkChange

Bayesian Hidden Markov Network Change Point Analysis

Detect structural changes in longitudinal network data using Bayesian multilinear tensor decomposition with hidden Markov models.

Overview

Many recorded network data span over a long period of time. Researchers often wish to detect "structural changes," "turning points," or "critical junctures" from these network data to answer substantively important questions. For example, the evolution of military alliance networks in international politics has been known to experience several breaks due to world wars and the end of Cold War. However, statistical methods to uncover network changes have been few and far between.

NetworkChange is an R package that detects structural changes in longitudinal network data using the latent space approach. Based on the Bayesian multi-array representation of longitudinal networks (Hoff 2011, 2015), NetworkChange performs Bayesian hidden Markov analysis (Chib 1998) to discover changes in structural network features across temporal layers.

Key capabilities:

Package Overview

Installation

# From GitHub (development version)
# devtools::install_github("jongheepark/NetworkChange")
install.packages("NetworkChange", repos = c('https://jongheepark.r-universe.dev', 'https://cloud.r-project.org'))

# From CRAN
install.packages("NetworkChange")

What's New in Version 1.0.0

Quick Example

library(NetworkChange)

# Generate network data with a structural break
set.seed(1973)
Y <- MakeBlockNetworkChange(n = 10, shape = 10, T = 40, type = "split")

# Fit model with 1 changepoint
fit <- NetworkChange(Y, R = 2, m = 1, mcmc = 1000, burnin = 1000, verbose = 100)

Visualization Gallery

1. Network Array Visualization

View snapshots of your network data over time:

plotnetarray(Y, n.graph = 4)

Network Array

Shows network structure at different time points to visually identify potential regime changes.

2. Latent Node Positions (plotU)

Visualize how nodes cluster in the latent space across different regimes:

plotU(fit)

Latent Node Positions

Each panel shows the latent positions for a detected regime. Node labels highlight the most influential actors (based on distance from origin).

Key features:

3. Layer-Specific Network Rules (plotV)

Track how network generation rules evolve over time:

plotV(fit)

Layer-Specific Network Rules

Each dimension captures a different aspect of network structure. Changes in V indicate shifts in how the network is generated.

Key features:

4. Contour Density Plot (plotContour)

Examine uncertainty in latent positions:

plotContour(fit)

Contour lines show the posterior density of node positions, revealing clustering patterns and estimation uncertainty.

5. Regime-Specific Cluster Analysis (drawPostAnalysis)

Perform k-means clustering within each regime:

p_list <- drawPostAnalysis(fit, Y, n.cluster = c(3, 3))
patchwork::wrap_plots(p_list, ncol = 2)

Identifies distinct groups of nodes in each regime with automatic cluster coloring.

6. Break Diagnostics (BreakDiagnostic)

Visualize the posterior distribution of changepoint locations:

BreakDiagnostic(fit)

Break Diagnostics

Shows when structural breaks occurred with uncertainty quantification.

Model Comparison

Compare Models with Different Numbers of Breaks

# Fit models with 0, 1, and 2 breaks
fit0 <- NetworkStatic(Y, R = 2, mcmc = 1000, burnin = 1000)
fit1 <- NetworkChange(Y, R = 2, m = 1, mcmc = 1000, burnin = 1000)
fit2 <- NetworkChange(Y, R = 2, m = 2, mcmc = 1000, burnin = 1000)

# Compare via WAIC
WaicCompare(list(fit0, fit1, fit2), print = TRUE)

# Compare via Marginal Likelihood
MarginalCompare(list(fit0, fit1, fit2), print = TRUE)

Model Comparison

Real Data Example: Major Power Alliances

# Load alliance network data (1816-2012)
data(MajorAlly)

# Fit model with 2 changepoints
fit <- NetworkChange(MajorAlly, R = 2, m = 2,
                     mcmc = 10000, burnin = 10000,
                     verbose = 1000)

# Visualize regime-specific latent spaces
plotU(fit, Time = 1816:2012)

# Layer-specific dynamics
plotV(fit)

# Cluster analysis
drawPostAnalysis(fit, MajorAlly, n.cluster = c(4, 4, 3))

Major Power Alliances - Latent Positions

Major Power Alliances - Cluster Analysis

Key Functions Reference

Function Purpose
NetworkChange() Fit changepoint model
NetworkStatic() Fit static model (no changepoints)
plotU() Latent node positions by regime
plotV() Layer-specific network rules over time
plotContour() Posterior density of latent positions
plotnetarray() Network snapshots over time
drawPostAnalysis() Regime-specific clustering
BreakDiagnostic() Changepoint location diagnostics
WaicCompare() Model comparison via WAIC
MarginalCompare() Model comparison via marginal likelihood

Customizing Visualizations

All plots use the unified theme_networkchange() theme:

library(ggplot2)

# Customize any plot
plotV(fit) +
  ggtitle("My Custom Title") +
  theme_networkchange(base_size = 14)

# Use package color scales
ggplot(data, aes(x, y, color = group)) +
  geom_point() +
  scale_color_networkchange()

Citation

If you use NetworkChange in your research, please cite:

Park, Jong Hee and Yunkyu Sohn. 2020. "Detecting Structural Change in Longitudinal Network Data." Bayesian Analysis. 15(1): 133-157.

@article{park2020detecting,
  title={Detecting Structural Change in Longitudinal Network Data},
  author={Park, Jong Hee and Sohn, Yunkyu},
  journal={Bayesian Analysis},
  volume={15},
  number={1},
  pages={133--157},
  year={2020},
  doi={10.1214/19-BA1150}
}

License

GPL-3

Authors