Social Network Analysis Report

How to produce a Social Network Analysis Report in Innoslate.

The Social Network Analysis Report can be generated using the Asset Diagram. To fully leverage the report's capabilities, it is essential for the model to satisfy certain prerequisites, ensuring the report's effectiveness. Below are the specified requirements.

Prerequisite

The following terms related to Social Network Analysis (SNA) are defined as they pertain to the Innoslate Schema:

• Nodes are referred to as Assets.
• Connections are designated as Conduits.
• The term Network Map corresponds to an Innoslate Asset Diagram.

Figure 1 below will serve as the main illustrative example throughout this article.

Figure 1: Example Network Map

4 Centralities needed to create this report:

• Degree Centrality
• Closeness Centrality
• Betweenness Centrality
• Eigenvector Centrality

Degree Centrality quantifies the number of direct connections an Asset has with other Assets. In Innoslate terminology, this indicates the number of "connected by" relationships associated with an Asset. For instance, as illustrated in Figure 1, the node demonstrates a degree centrality score of 3.

                                         Figure 2: "Connected By" Relationships-SNA Report

Closeness Centrality evaluates the efficiency with which an Asset can reach all other Assets within the network, based on their relative distances. To compute Closeness Centrality, please follow these steps:

1. Identify the shortest path between each pair of Assets. In Innoslate, the shortest path is defined as the route connecting two Assets with the minimum number of Conduits.
2. For each Asset, calculate the total length of all shortest paths to every other Asset (e.g., sum the Conduits between Node #1 and Node #2, Node #1 and Node #3, etc.).
3. To derive the normalized score, execute the following:
   1. a. Take the total number of Assets (e.g., 6) and subtract 1 from this figure (e.g., 6 – 1 = 5).
   2. b. Divide the result from Step 3.a (e.g., 5) by the total in Step 2 (e.g., for Node #1, 5 / 7 = 0.714).

Betweenness Centrality assesses the frequency with which an Asset appears on the shortest path between two other Assets, effectively serving as a choke point in the flow of information. For instance, in Figure 1, Node #2 functions as a critical conduit for information transfer to Nodes #3 and #4. To calculate Betweenness Centrality, follow these steps:

1. Identify all unique pairs of Assets within the network (e.g., Nodes #1 and #2, #1 and #3, and so on).
2. Count the total number of shortest paths existing between each pair of Assets.
3. Analyze the shortest paths and note how frequently a specific Asset (e.g., Node #1) appears on any of these paths.
4. For each pair, divide the count from Step 3 by the total from Step 2.
5. Sum all the calculated values from Step 4 to arrive at the betweenness centrality score.

Eigenvector Centrality evaluates the significance of an Asset by considering the importance of its connected neighbors. Unlike Degree Centrality, which focuses solely on the quantity of connections, Eigenvector Centrality emphasizes the quality of those connections. To compute Eigenvector Centrality, please adhere to these steps:

1. Calculate the Asset Diagram’s adjacency matrix.
2. Find the largest eigenvalue of the adjacency matrix.
3. Find the corresponding eigenvector to the largest eigenvalue.
4. Scale the eigenvector so that its largest entry is 1, if needed. This achieves a normalized score.

How to Create the Social Network Analysis Report

1. Navigate to the specific Asset Diagrams that include the required centralities mentioned in the above prerequisites.
2. Find and Select 'Report' on the toolbar.
3. Select 'Social Network Analysis.' The CSV download will automatically begin.