Visual Network Mapping
I enjoyed this exercise and investigated various software for creating visual network maps, including Gephi, TouchGraph, NodeXL, and PowerBi. I ultimately decided to generate a social network analysis of my LinkedIn profile using Kumu https://https//kumu.io. I chose to use my LinkedIn network as my data source because I believed it would be an interesting way to visualize my professional network, which I’ve been a part of since August 12, 2010. I have 404 first-degree connections on LinkedIn at present. I utilize LinkedIn to network, learn from this community, stay current on new industry trends and developments, and enjoy the informal learning opportunities that such conversations provide.
It was fascinating to see that in their typology of Social Forms for Learning, Anderson and Dron (2014) include networks as a key structure, contending that networks have always been channels for information and knowledge sharing. The authors further state that “Networks consist of nodes—such as people, objects, or ideas—and edges, the connections between them. In the social form of a network, networks connect distributed individuals and groups of individuals, one node and edge at a time” (p. 76). As a person who enjoys learning and exploring new ideas, I consider these environments in which one can acquire knowledge and learn at one’s own pace to be a vital part of my life.
How to create a Social Network Analysis (SNA) Map
I followed the steps listed below to create the Social Network Analysis (SNA) map using Kumu for my LinkedIn Network.
Download “Connections” Data File from LinkedIn:
I downloaded a CSV file containing my connections from LinkedIn. The LinkedIn Connections data file includes the columns First Name, Last Name, Email Address, Company, Position, and “Connected On” date.
Clean up Data File for Kumu:
I cleaned up this file using the following procedures:
- I removed the first three rows from the CSV file containing notes about the file.
- The “Connected On” date column was removed.
- I merged the first and last name columns into a single column containing both names and changed the column’s heading to “Label.” The original first and last name columns were removed.
- I added a new column next to the “Label” column and titled it “Type” and I entered “Person” for each entry in that column, so that the type column next to each person’s name reads “Person.” So that the software knows each label (first and last name) corresponds to an individual.
I changed the column header from “Company” to “Organization.” - I added a new column titled “Organization Type” next to the organization column and labeled all organizations as belonging to certain categories, such as healthcare, education, and information technology. After experimenting with the network map in Kumu, I added this column to see a cluster of my connections from industries in which I have worked, such as healthcare, education, and information technology. I manually compiled this information for my 404 connections, this was a time-consuming task.
- I changed the column heading from “Position” to “Title.”
- I eliminated 12 connections that lacked organization-specific information.
I then uploaded my CSV data file to Kumu, experimented with various mapping templates, and found the Stakeholder template and SNA template particularly intriguing.
Social Network Analysis (SNA) Maps:
Here’s are some of my LinkedIn SNA maps that I created using the above steps:
Figure 1: Each blue node in the map above represents a person in my LinkedIn Network who is my first degree connection. The yellow node represents the organization to which that specific person is affiliated.
Figure 2: Each cluster in this map represents a type of organization or sector, and each individual working in that sector is depicted within that cluster. The large cluster in the center represents my connections who work in healthcare. The other three large clusters represent my connections in the information technology, education, and insurance industries.
Figure 3: There are several different templates available in Kumu for creating visual network maps. I used stakeholder template to create the above map. Each template is highly customizable with a multitude of options, such as how you wish to represent your data and how you wish to create the connections between nodes. In addition, there is the option to filter elements and connections, highlight specific elements, and build bridges between mutual connections. Each white node represents an individual, while each blue note represents an organization with which that individual is affiliated.
It was a fascinating exercise that provided me with an in-depth look at my LinkedIn network in a way I had never seen before.
Reference:
Dron, J., & Anderson, T. (2014). Teaching Crowds. AU Press. https://doi.org/10.15215/aupress/9781927356807.01