Network Plot Example

Network Plot Example

Problem Statement

Using the Cars data, create an edge list network plot beween cyl and disp. Data is attached in the attachment.

How to perform analysis

Step 1: Open Sigma Magic
  1. Click on the Sigma Magic button on the Excel toolbar.
  2. Click on the New button to create a new project.
Step 2: Add the analysis template
  1. Click on the Tool Wizard to add the analysis template.
  2. Click on Analytics and then Network Plot.


Step 3: Specify analysis options
A new worksheet will be added to your workbook. Analysis Setup will be automatically opened, in the setup tab specify the survey results.



Click on Data to specify the data required for this analysis.



If you need to make changes to the charts, specify the optional settings in the Charts tab.

 Labels:

  • Add a title for the chart.
  • Label the X-axis and Y-axis appropriately.

Appearance:

  • Adjust colors, font sizes, or other visual elements as needed.
  • Enable/disable gridlines or background shading.


Click the Verify tab to ensure all the inputs are okay and shown in a green checkmark.



Step 4: Generate analysis result
Click OK and then click Compute Outputs to get the final results.



Interpretation of Results

  • The data type is an edge list, meaning it represents relationships between entities.
  • The mode is "Undirected", indicating that the connections between nodes are bidirectional (no hierarchy or flow direction).
  • Two variables (cyl and disp) are used for the network structure.
  • Likely, cyl (cylinder count) and disp (engine displacement) are linked to form clusters.
  • The layout is set to Auto, meaning the software has automatically arranged the nodes.
  • Nodes are represented as yellow circles with labels, showing connections through edges.
  • The network consists of three major clusters (groups of interconnected nodes).
  • Each cluster has a central node with multiple surrounding nodes.
  • Certain nodes act as central points in each cluster, having the highest number of connections (high degree centrality).
  • For example, node 2 in the bottom-left cluster has many direct links.
  • The edges (lines) between nodes indicate relationships between the entities.
  • These could represent similarity in values (engine specs) or frequency of interactions between components.
  • The clusters may indicate different categories of vehicles based on their cylinder and displacement values.
  • If applied in a business context, this could represent customer segmentation or product classification.
  • Some nodes have single connections (leaf nodes), meaning they are less connected or outliers in the dataset.
  • Nodes with multiple connections are more integrated into the network.
  • The software confirms that "Analysis Created", meaning the input data was processed successfully.
  • The conclusion states "Network diagram has been created", confirming the plot’s correctness.
  • The plot can help identify key influencers (central nodes) in a social network.
  • In business, it can segment product categories, detect fraud networks, or optimize workflows.
  • In engineering, it may group engine configurations with similar performance metrics.

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