Tuesday, November 6, 2018

Summary of Findings: Social Network Analysis (3.5 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in November 2018 regarding Social Network Analysis as an Analytic Technique, specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Social Network Analysis (SNA) is a method for analyzing social structures based on connections/relationships (links) between individuals (nodes). It is applicable to a wide variety of disciplines, demonstrating its flexibility. With a robust data set, the visual outcome can be exceptionally detailed. Regarding the data-- SNA requires structured data, and expertise in manipulating that data accurately. While a moderate level of knowledge and expertise is necessary to properly execute this method, getting there is fairly simple.

  1. Provides a holistic picture of a network
  2. Maps the spread of ideas
  3. Determines who the most influential individual(s) in a network is
  4. Provides an alternative view where attributes of individuals are less important than their relationship ties
  5. Can help analysts identify the most powerful nodes in a network
  6. Can be used to simulate how information ripples through a network
  7. Can be used to identify intelligence gaps

  1. Defining relationships and understanding what that relationship means is difficult
  2. Visualization does not always tell a clear story, making it difficult to communicate to a DM
  3. SNA for ambiguous organizations may cause confirmation bias
  4. Constructing a matrix can be time consuming
  5. Matrix attributes can be subjective and often biased
  6. This tool can only be used in some situations, not all cases are applicable for social network analysis
Exploring the full scope of social network analysis and how to do it properly was beyond the scope of this assignment.  Therefore, a simplified form of the process was developed that would allow participants in the exercise to experience some of the challenges as well as the benefits of performing a social network analysis.  The “rules” of this simplified process are below:
  1. Write out the names of the last 10 people you exchanged text messages with
    1. Exclude group chats
  1. Pick one person to write the names of those 10 people around his/her own name on a sheet of paper or whiteboard, with ample space to draw connections between names.
    1. The first person is to then connect his/her own name to all those in his/her own network.
    2. The first person will then connect those in his/her network to each other based on whether they know each other.  
  1. The second and third participants draw out their own networks repeating steps 1 and 2
    1. Important: any repeat names within each other’s network can pose a problem. We recommend omitting repeat names if a person is already listed in another participant’s network. The connections will ultimately be made in step 4.
  1. Connect participants’ networks to each others based on whether they know each other.
  2. With all people listed in the network, build a matrix of network connections.
  3. Build a frequency table to determine who in the network has the most connections.
The participant with the most connections is the most involved in the social network.

Application of Technique:

The Advanced Analytic Techniques (AAT) class was presented the following scenario to conduct a social network analysis: A transfer student was new to the program and on the first day sat at a table with 3 classmates. The task for the activity was to determine which of the classmates at the table had the most connections to others in the class’s network. For the instructions the AAT class was given to complete the task, see the “How-To” section.

Figure 1. Individual Networks

Figure 2. Connecting individual networks to each other
Figure 3. Matrix based on relationships

Figure 4. Frequency Table

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