🕸️ Modeling Influence, Not Just Users
Turning relational data into decision-ready segments for product strategy.
Most analytics treats users independently. I model influence as structure.
Models
influence pathways (not averages)
Enables
where to intervene first
Outputs
actionable segments + experiment plan
Decision pipeline
Product question → relational signals → structure → roles → segments → experiments
Product Challenge
Social platforms struggle to answer:
- Where should we intervene first?
- Which users actually shape outcomes?
- Why do global averages fail to explain behavior?
Why Traditional Analytics Fails
Killer differentiator: influence is structural.
Typical analytics
- ❌ treats users independently
- ❌ ignores relational structure
- ❌ optimizes averages instead of influence pathways
This leads to
- misidentified “power users”
- ineffective interventions
- wasted experimentation cycles
Decision Pipeline
- Product question (e.g., where to intervene, who to prioritize)
- Relational signals (interactions, nominations, communications)
- Network structure (communities + interpretable topology)
- Influence roles (central users, bridge users, local hubs)
- Actionable segments (segment-level measurement + targeting)
- Experiment design (intervention + success metrics by segment/role)
The goal isn’t “a nice graph.” It’s a framework that turns relational data into decision-ready segmentation.
Structural Insights
Insight 1 — Influence is uneven
Small subsets of users drive norm formation.
Insight 2 — Bridge users are leverage points
Changing bridges affects multiple communities simultaneously.
Insight 3 — Central ≠ positive
High centrality requires contextual validation.
What Teams Can Do With This Insight
- Communities targeted onboarding (tailor activation + retention by segment)
- Bridges cross-community experiments (move information across clusters)
- Influence roles safety or growth interventions (prioritize role-based levers)
This is the hiring-manager translation: “this person helps teams decide where to intervene.”
Evidence Base
This framework builds on:
- longitudinal network research
- multi-level modeling of influence roles
- validated leadership detection algorithms
Research-backed patterns (generalized)
- Roles move. Influence positions change over time—treat segmentation as something you refresh on a cadence, not a one-time label.
- Influence is multi-level. Some effects are local (within a cluster), others are cross-cluster—choose the level that matches the decision.
- Bridges are high leverage. Small sets of connectors can shift outcomes across multiple clusters at once—great candidates for targeted experiments.
- Separate “leader signals” from the baseline. High-visibility role holders and the broader segment norm can each matter independently—measure both.
- Different roles → different levers + metrics. Leader signals often align with behavior-control outcomes, while baseline norms align with belonging / well-being—instrument accordingly.
Source example (open access): 10.1111/jora.70143
(Details are presented in an NDA-safe way; the emphasis here is on decision translation.)
How This Looks in Practice
NDA-safe case summary format (interview-ready).
- network snapshot + segment labels
- decision recommendation (what to do first, and why)
- experiment plan (who to target, what to change, what to measure)
Network Analysis → Product Language
Hiring manager mental model translation.
Research term
Product meaning
community detection
user segmentation
centrality
influence strength
bridges
growth leverage