Harsh RB

Visualizing and Interpreting Facebook Networks

4 min read

4.1.1 Introduction: The World’s Social Graph

  • Publicly Articulated Networks: Facebook represents a unique class of networks. They are not based on invisible behavior (like email traffic) but on the relationships we intentionally “show” to others to manage access to information.

  • Scope: At the time of writing, Facebook was the largest social network (~400 million members), with the News Feed acting as the primary stream of information shared between friends.

4.1.2 Historical Context

  • Growth Strategy: Unlike MySpace (which allowed total customization), Facebook used exclusivity. It started at Harvard, moved through specific universities, and finally opened to the general public.

  • Network Effects: By targeting universities, Facebook benefited from pre-existing real-world connections.

  • Contradiction: Facebook provides granular privacy tools but pushes “information libertarianism” by making data as public and discoverable as possible (e.g., the introduction of the News Feed in 2006).

4.1.3 Why Map a Facebook Network?

  1. Privacy Management: Identifying if a specific group (like “Family”) is truly separate from other clusters.

  2. Networking Style: Identifying if you are a Team Player (closing triads/introducing people) or a Broker (keeping groups separate to maintain strategic value).

  3. Strategic Planning: Event planners use maps to see if their audience is one dense cluster or multiple disconnected groups.

  4. Social Hygiene: Finding “zombie” contacts added years ago but never engaged with.

4.1.4 What Kind of Network is Facebook?

Facebook friendship networks are Egocentric Networks.

  • Ego: The focal person (the owner of the network).

  • Alters: The friends connected to the Ego.

  • Network Levels:

    • 1.0 Degree: Just Ego and their alters (a simple star shape).

    • 1.5 Degree: Ego, alters, and the connections between those alters. This is what NodeXL maps.

    • 2.0 Degree: ego, alters, and their friends (including people Ego doesn’t know). Facebook’s API does not allow this.

  • Properties:

    • Undirected: Friendships must be mutual.

    • Unweighted: By default, all friend connections are treated as equal.

4.1.5 - 4.1.6 Basic Visualization in NodeXL

  • Data Source: Traditionally imported via apps like NameGenWeb using the GraphML format.

  • Hiding Ego: In an ego network analysis, it is standard practice to exclude the owner (Ego). Since Ego is connected to everyone, including them clutters the graph and obscures the internal clustering of friends.

  • The “Networky” Look:

    • Layouts: Fruchterman-Reingold and Harel-Koren are best.

    • Iterations: Default settings (10 iterations) are often too low for Facebook. Increasing to 80-100 iterations helps resolve distinct clusters.

4.1.7 Data Types and Attributes

Categorical (Nonordered) Data

  • Examples: Gender, Hometown, Cluster ID.

  • Visual Mapping: Use Color or Shape.

  • VLOOKUP Strategy: Advanced users create a “Categories” worksheet to manually assign colors/shapes to groups like “Male,” “Female,” or “Unknown.”

Numerical (Ordered) Data

  • Examples: Degree, Betweenness Centrality, Age.

  • Visual Mapping: Use Size or Opacity.

  • Key Interpretations in Ego Networks:

    • Degree: The number of mutual friends between you and that person.

    • Betweenness: Identifies Bridges. A person who links your high school friends to your work colleagues has high betweenness and likely knows you very well.

    • Eigenvector Centrality: Identifies those in the center of dense clusters. Mapping this to opacity can make dense groups “glow” or become transparent to see the structure inside.

4.1.8 From Friend Wheel to Pinwheel

  • Friend Wheel: A popular radial layout where nodes are arranged in a circle. It is aesthetically pleasing and avoids node overlap, but can be hard to interpret.

  • Pinwheel Layout (The NodeXL Way): A customized clustered radial layout.

    • Logic: Groups are arranged in “flames” or wedges around a circle.

    • Mapping: Radius is scaled to Betweenness (pulling brokers toward the center), while Size/Color are mapped to Degree.

    • Benefit: Reveals the internal density of a cluster while simultaneously showing how that cluster links to the rest of the network.

4.1.9 - 4.1.10 Practitioner and Researcher Summary

  • Practitioners: Facebook maps reveal the “social context” of your life. Using Excel features like VLOOKUP and LOG formulas allows for much more sophisticated visuals than the standard “one-click” apps.

  • Researchers: * Clustering Limits: Is it more useful to see “Work” as a group, or to find the “soft partition” of people who belong to both “Work” and “Sports”?

    • Dunbar’s Number: Human brains have a cognitive ceiling of about 150 stable relationships. Does Facebook act as a “cognitive enhancement” to break this limit, or just lead to information overload?

Exam Tip: Be prepared to explain why Betweenness Centrality is used to identify “Bridges” in Facebook and why the Ego should be removed before calculating metrics.

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