Harsh RB

SNA 2.3

3 min read

Preparing Data and Filtering

2.3.1 Introduction

  • Challenge: Large networks are often too dense to visualize clearly.

  • Core Strategies:

    1. Summarization: Rolling up relationship data into a weighted form (e.g., merging duplicate interactions).

    2. Filtering: Removing selected vertices or edges to identify extreme values or specific subsets.

  • Goal: Create understandable visualizations by reducing edge crossings and vertex overlaps.

2.3.2 Serious Eats Network Example

The Serious Eats Network is based on community members (people) posting to blogs and discussion forums.

2.3.2.1 Multimodal Network Data

  • Multimodal (Bimodal/Two-Mode): A network with different types of vertices (e.g., people and the blogs/forums they post to).

  • Affiliation Networks: A specific type of bimodal network connecting people with events or organizations they are affiliated with.

2.3.2.2 Merging Duplicate Edges

  • Process: NodeXL’s “Merge Duplicate Edges” feature condenses multiple identical rows into one.

  • Result: A new Edge Weight column is created, indicating how many times that specific connection occurred. No data is lost; the weights simply summarize the frequency.

2.3.2.3 - 2.3.2.6 Visualizing Multimodal Data

  • Sorting: Alphabetical sorting (A to Z) helps group vertices of the same type (e.g., those starting with “B_” for blog or “F_” for forum).

  • Visual Coding:

    • People: Black disks.

    • Blogs: Blue solid diamonds.

    • Forums: Orange solid squares.

  • Layout Management: The “Put smaller components at the bottom” option helps separate isolated groups from the “Giant Component.”

2.3.3 Filtering Strategies

Filtering reduces clutter to reveal hidden structures or important features.

Types of Filtering:

  1. Value-based: Removes items above or below a numerical value (e.g., age, degree, or edge weight).

  2. Categorical: Retains or removes items based on a category (e.g., region or gender).

  3. Ordinal: Filters by rank (e.g., showing only the “Top 10” most connected users).

2.3.3.1 Dynamic Filters

  • Function: Real-time sliders that hide/show data in the graph pane without deleting it from the workbook.

  • Frequency Distributions: Histograms above sliders show the concentration of data at different values.

  • Filter Opacity: Allows “filtered out” items to remain visible as faint “ghost” images (e.g., setting opacity to 10%).

2.3.3.2 Filtering via Visibility Column

  • Method: Using the Autofill Columns feature to populate the “Visibility” column.

  • Difference from Dynamic Filtering: Items marked as “Skip” in the visibility column are not read into the graph at all. This allows layout algorithms to treat the remaining nodes as the entire network.

2.3.3.3 Subgraph Images

  • Egocentric Networks: Visualizing the “local neighborhood” of a single vertex.

  • Levels of Adjacency: * 1.0: The vertex and its neighbors.

    • 1.5: The vertex, its neighbors, and connections between those neighbors.

    • 2.0: Includes “friends of friends” (FOAF).

  • Purpose: Helps identify social roles (e.g., distinguishing between a user who posts in isolated topics vs. a “hub” user).

2.3.4 - 2.3.6 Summary and Research

  • Practitioner Perspective: Filtering is an iterative process. Analysts use dynamic filters to find a threshold and then hard-filter (using Visibility) to create a clean, persuasive final image.

  • Researcher Perspective: Future goals include Process Models—standardized sequences of actions (filtering, layout, and metrics) that ensure a complete and systematic exploration of social media data.

Study Tip: Remember the difference between Hidden/Hide (Dynamic Filters) and Skip (Visibility Column). Skipping removes the data from metric calculations and layout processing; Hiding just makes it invisible.

Graph View

Backlinks