SimulationsProject-BasedData Modeling

Build a Classroom Campaign: Simulate Social Network Dynamics with Physics-Based Models

UUnknown

2026-02-14

9 min read

Turn network theory into a hands-on class campaign: students simulate diffusion and percolation of information, inspired by Bluesky's 2026 rollouts.

Hook: Turn Confusion into a Campaign — Teach Network Dynamics with a Live, Physics-Based Lab

Students struggle with abstract models, lack structured practice, and rarely see how physics modeling maps to current events. Run a project-based campaign where learners design, simulate, and evaluate information spread on networks using diffusion, percolation, and epidemic-style models — all inspired by real 2026 events like Bluesky’s cashtags and LIVE badges rollouts. This activity gives students hands-on experience with network dynamics, measurable outcomes, and concrete visualizations they can present in class or to stakeholders.

Why this Project Matters Now (2026 Context)

In late 2025 and early 2026, social platforms reacted to waves of media attention and trust failures. Bluesky’s feature rollouts (specialized cashtags and LIVE badges) — and the surge in installs following deepfake controversies on competing platforms — make a compelling, real-world case study for how a small change in platform mechanics can alter how information spreads.

"Discoverability is no longer about ranking first on a single platform; it’s about showing up across the touchpoints that make up your audience’s search universe." — 2026 trends in social search and digital PR

Students who can link physics-style models to platform tweaks will better understand modern information ecosystems and be prepared to analyze interventions (content tagging, throttling, promotion) using a rigorous, quantitative toolkit.

Learning Goals & Outcomes

Understand and implement diffusion, percolation, and basic epidemic (SI/SIR/SIS) models on networks.
Compare how node/edge-based interventions (e.g., badges, boosted posts) change reach and speed of information spread.
Visualize outcomes and interpret metrics: final size, peak spread time, outbreak probability, and percolation threshold.
Develop reproducible simulation code, data visualizations, and a short classroom presentation or report.

Classroom Logistics

Duration: 2–4 weeks (modular; fits semester or intensive workshop).
Group size: 3–5 students per team (roles: modeler, data analyst, visualization/presenter).
Prerequisites: basic programming (Python recommended), basic graph theory, introductory statistics.
Tools: Python (NetworkX, numpy, matplotlib/plotly), NetLogo, Gephi, Observable notebooks, or web-based platforms (Binder/Colab) for remote classes.

Core Concepts to Teach (Brief)

Cover these topics in introductory lectures or reading modules before students start coding:

Network types: Erdős–Rényi (random), Barabási–Albert (scale-free), Watts–Strogatz (small-world).
Percolation: site vs. bond percolation; percolation threshold and giant-component formation.
Diffusion & Epidemics: SI, SIS, SIR models; transmission probability and recovery.
Seeding strategies: random seeds, high-degree (hubs), community-targeted seeds, influencer boosts (analogy to badges).
Measurements: final reach (fraction of nodes informed), time to peak, outbreak probability, path lengths used.

Project Overview: The Campaign Phases

Design: choose network model, seeding strategy, and intervention inspired by Bluesky features.
Implement: code the simulation (percolation or epidemic) and set reproducible parameters.
Experiment: run parameter sweeps to see how outcomes depend on transmission probability, seeding location, and feature boosts.
Analyze: compute metrics and create visualizations; estimate percolation thresholds where relevant.
Report & Present: compare strategies, discuss real-world implications & ethics.

Detailed Step-by-Step Activity Plan

Step 1 — Build the Network

Choose one or more network models to test. For authenticity, run experiments on multiple topologies.

Erdős–Rényi: good baseline for random connections.
Barabási–Albert (scale-free): mimics influencer-dominated social networks.
Watts–Strogatz (small-world): captures clustering and short path lengths.

Key parameters: node count N (e.g., 1,000–10,000 for class machines), mean degree, and rewiring probability (for small-world networks).

Step 2 — Choose the Model of Spread

Select one of these modeling frameworks:

Percolation (static): Site percolation models nodes that are 'open' (willing to share) or 'closed'; bond percolation models open edges. Good for studying platform feature adoption thresholds.
SI (Susceptible-Infected): Simple diffusion where once a node is informed it remains so — useful for viral posts with persistent visibility.
SIR (Susceptible-Infected-Recovered): Nodes can recover/lose interest; good for modeling attention decay or content moderation removal.
Threshold models: Nodes adopt only if a fraction of neighbors adopt; parallels social reinforcement like trending labels.

Step 3 — Seed Strategies & Bluesky-Inspired Treatments

Frame experiments around feature analogues:

Cashtags (targeted discovery): Model as boosted visibility for posts carrying a tag that connects topic-based communities; simulate by temporarily increasing transmission probability along topic edges or boosting node visibility within topic clusters.
LIVE badges (signal of activity): Model as a burst seeding strategy where active nodes generate repeated exposures for a time window — implement as periodic re-seeding of the same nodes.
Boosted installs (growth surge): Simulate an increase in node count or new high-degree nodes joining during the campaign to study how late arrivals affect spread.

Compare seeding at hubs, random nodes, and community boundary nodes to see how the platform features change the effective reach.

Step 4 — Run Simulations & Parameter Sweeps

Run Monte Carlo experiments (50–200 runs per parameter set) and sweep important parameters:

Transmission probability p (or bond occupation probability).
Fraction of initial seeds.
Intervention strength (boost multiplier for p or periodic reseeding rate).
Timing of feature rollout (early vs. late in the campaign).

Keep experiments reproducible with fixed random seeds and documented configs (JSON/YAML).

Step 5 — Metrics & Data Analysis

Compute and plot:

Final reach: fraction of nodes ever informed.
Time-series: informed fraction vs. time; identify peak time and sharpness of spread.
Outbreak probability: fraction of runs that exceed a chosen final-size threshold.
Percolation threshold estimate: critical p where giant component appears (scale-free networks may have no clear threshold).
Comparative plots: show how interventions shift curves or thresholds.

Worked Example (Compact Python Walkthrough)

Below is a minimal SIR-style simulation using NetworkX. This is a scaffold students can expand for parameter sweeps and plotting.

# Python (simplified)
import networkx as nx
import random
import numpy as np

def run_sir(G, p_transmit, p_recover, seeds, max_steps=100):
    state = {n: 'S' for n in G}
    for s in seeds:
        state[s] = 'I'
    history = []
    for t in range(max_steps):
        new_state = state.copy()
        for n in G:
            if state[n] == 'I':
                for nbr in G[n]:
                    if state[nbr] == 'S' and random.random() < p_transmit:
                        new_state[nbr] = 'I'
                if random.random() < p_recover:
                    new_state[n] = 'R'
        state = new_state
        history.append(sum(1 for v in state.values() if v == 'I')/len(G))
        if all(v != 'I' for v in state.values()):
            break
    return history, state

# Example usage
G = nx.barabasi_albert_graph(1000, 3)
seeds = random.sample(list(G), 5)
hist, final = run_sir(G, p_transmit=0.03, p_recover=0.1, seeds=seeds)
print('Final informed:', sum(1 for v in final.values() if v in ('I','R'))/len(G))

Students should add plotting (matplotlib/plotly), run multiple seeds for averages, and test interventions like multiplying p_transmit for nodes with a simulated badge.

Interpreting Results: What to Expect

Typical observations students will report:

On scale-free networks, seeding hubs rapidly increases reach; the network may be vulnerable to fast, large cascades even at low p.
On random networks, there’s usually a clearer percolation threshold p_c where a giant informed component appears.
LIVE-style periodic boosts accelerate time-to-peak and increase outbreak probability but may not change final size if recovery dynamics dominate.
Cashtag-like targeted boosts can be very efficient when topic clusters are well aligned with network communities — they increase within-community spread but may need cross-community bridges to achieve global reach.

Ethics, Safety, and Real-World Context

Because the prompt connects to real-world controversies (e.g., deepfakes and platform trust in early 2026), include an ethics module:

Discuss consent, misinformation, and how platform design amplifies harms.
Emphasize synthetic data and anonymized, permissioned datasets when exploring real platform-like behavior.
Encourage teams to propose mitigation strategies from the simulations: rate-limiting, detection + removal, community-based warnings, or changing recommendation algorithms.

Assessment Rubric & Deliverables

Grade each group on:

Model & Implementation (30%): Clear code, reproducibility, and correctness.
Experimental Rigor (25%): Parameter sweeps, Monte Carlo runs, and consistent metrics.
Analysis & Insight (25%): Interpretation of results tied to real-world platform features and 2026 trends.
Communication & Visuals (10%): Clear plots, figures, and a 5–10 minute class presentation.
Ethics & Reflection (10%): Address harms, biases, and discuss mitigation strategies.

Advanced Extensions (For Longer Projects)

Parameter inference: Fit a simple SIR model to synthetic or API-provided adoption data and estimate effective reproduction numbers.
Algorithmic promotion: Simulate a recommendation algorithm that preferentially boosts high-engagement nodes and measure feedback loops.
Co-evolution: Let network topology evolve as users follow or unfollow during the campaign and study long-term dynamics.
Multi-platform spread: Model cross-platform contagion where nodes exist on multiple overlapping networks (e.g., Bluesky + X + TikTok).

Practical Classroom Tips & Troubleshooting

Start small: N=500 nodes for first runs to ensure fast iteration; scale up once code is stable.
Automate runs with scripts and log configs so results are reproducible and auditable.
Use visualization early — even hand-drawn network snapshots help students form intuition.
Teach statistical thinking: show variance across runs and avoid overinterpreting single outcomes.
Leverage 2026 AI tools for scaffolding (code generation, plotting helpers), but require students to explain and validate generated code. AI can speed development but not replace conceptual understanding.

Classroom Example Timeline (2-Week Intensive)

Days 1–2: Intro lectures & tool setup.
Days 3–4: Network building & simple SIR runs.
Days 5–7: Implement Bluesky-inspired interventions and run sweeps.
Days 8–10: Analysis, visualization, and report writing.
Day 11: Presentations and reflection on ethics and real-world implications.

How This Links to 2026 Trends in Discoverability and Platform Design

Recent discourse in 2026 emphasizes cross-platform discoverability and the role of social signals in shaping attention. Platforms now combine algorithmic promotion, tagging systems, and visible activity cues — systems that classroom teams can model as adjustable parameters. By exploring how small feature changes produce big differences in outcomes, students learn to think like designers and regulators, not only mathematicians.

Final Takeaways & Actionable Checklist

Set clear learning objectives and choose network sizes appropriate for your computing resources.
Teach 2–3 core models (percolation, SI/SIR, threshold) and have students implement at least one fully.
Design experiments inspired by real feature rollouts (cashtags, LIVE badges) — contrast targeted vs. broad boosts.
Measure multiple metrics and run Monte Carlo experiments to quantify uncertainty.
Include an ethics module; require reflection on harms and mitigation strategies.

Call to Action

Ready to turn abstract network theory into a compelling classroom campaign? Download our free Classroom Campaign Kit (lesson plans, reproducible Python templates, rubrics, and slides) and run your first diffusion and percolation labs this term. Invite students to submit short reproducible notebooks and host a mini-conference to showcase findings — and if you try a Bluesky-inspired experiment, share your anonymized results with our teaching community for feedback.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.