Cookbook: Social Network Influence Analysis

A complete walkthrough from interaction data to influence evolution tracking.

This cookbook demonstrates how to analyze social networks temporally - tracking how influence emerges, communities form, and content spreads over time. We'll use synthetic social interaction data.

The Challenge

Social platforms generate millions of interactions, but:

1. Static follower counts lie - engagement matters more
2. Influence shifts over time - yesterday's star is today's nobody
3. Communities evolve - echo chambers form and fragment
4. Viral spread has a timeline - tracking who spread what, when

What we'll analyze:

• Influence evolution (who's rising, who's falling?)
• Information cascade modeling
• Community formation over time
• Coordinated inauthentic behavior (bot detection)

The Data Model

Step 1: Generate Social Interaction Data

We'll create synthetic data representing a social platform with various interaction types.

Output:

Step 2: Build the Multi-Layer Social Graph

Use layers to separate different interaction types for nuanced analysis.

Step 3: Track Influence Evolution

Compare PageRank scores over time to identify rising and falling influencers.

Output:

Step 4: Model Viral Content Spread

Trace how content spreads through the network in chronological order.

Step 5: Detect Community Formation

Track how communities emerge and evolve over time.

Step 6: Detect Coordinated Inauthentic Behavior

Find potential bot networks by identifying accounts created together with synchronized activity.

Summary

This cookbook demonstrated a complete social network analysis pipeline:

Key temporal insights:

• Influence is dynamic - weekly snapshots reveal who's rising/falling
• Virality has a timeline - first shares within minutes, cascade over hours
• Communities evolve - fragmentation and consolidation over time

Next Steps

• Louvain Community Detection – Algorithm details
• PageRank Reference – Influence ranking
• Temporal Reachability – Spread modeling