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Network Extractor

 1 Minute



What Is It?

Virtualitics' Network Extractor is a routine that enables users to generate Network Graphs from tabular datasets.

Why Is This Important?

The Network Extractor is an incredibly powerful tool for visualizing similarities and identifying groups, or communities, within your data. This enables users to build a Network Graph and utilize Network Analysis Tools to understand the characteristics of the communities within the data.

For example, extracting a network from a dataset of user characteristics can help to better understand users' purchasing behavior and segmentation based on categorical features, which would otherwise be very difficult to do for a non-data scientist.


The Network Extractor accepts as input a target feature which will become the nodes (things) in the network, as well as whichever associative features you would like to use to define the edges (similarities) in the network.

Steps to create a Network Graph using Network Extractor:

  1. Open the Network Extractor panel by clicking the icon in the toolbar or selecting Network Extractor from the Data Analytics menu.
  2. Drag and drop a feature into the Target box to define your nodes.
    • The Target can be any feature in your dataset that can be used to distinguish between nodes in your network. Often, some type of ID field works best. A few examples include:
      • A patient ID field in a dataset containing patient records to create a network of patients
      • A stock ID field in a dataset containing stock performance data to create a network of stocks
      • A User ID field in a dataset containing user purchasing data to create a network of users
    • This field can be unique or non-unique. If there are duplicate values in the Target feature, the dataset will be aggregated using a method in the dropdown list (defaults to Mean) 
  3. Now, determine what Associative Features you would like to use to generate your Network Graph.
    1. These features will be utilized to construct associations between the nodes, signifying similarity. 
  4. Add other features by dragging and dropping them into the Associative Features area or using the Input All button.
  5. (Optional) Remove unwanted features by hovering over the feature and clicking the "x" button. 
    1. Note: Any rows with missing values will not be visualized in the Network Graph, so you may want to remove sparse features (with many missing values). Click the red pound sign to remove sparse features. 
  6. Once the Target and Associative Features are determined, click the "Run" button.

Note: We typically recommend running Network Extractor on datasets with fewer than 20,000 - 30,000 nodes for fast runtimes.

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