Social Network Analysis

Investigating Social Structures Through Networks and Theory

About SNA and Social Network Diagrams

Social Network Analysis (SNA) helps characterize networked structures in terms of nodes. These structures show nodes (individual actors, people, or things residing within the network) and the ties, edges, or links (interactions) that connect them. Below is a picture of a node connecting users interacting with one another on Twitter.

The different colored clusters represent a group of people connected to one another through one central topic. These social structures are often characterized by dense clusters of strong connections; in the image above, you can observe over 20 dense clusters modeled by the network.

The Importance of SNA

There are many practical applications for Social Network Analysis. By using SNA and network modeling, we can observe a wide range of patterned behavior across a broad spectrum of disciplines. Here at the RAVE Lab, we are utilizing it for business intelligence purposes as well as analysis of individual and group engagement.

For our SNM project, we are currently data mining and modeling social networks in an attempt to help our marketing efforts. By modeling the network for #Solar, we can identify viral influencers and begin to understand how their content goes viral; we categorize their content and piece by piece build a formula that represents their viral post. We then try this formula with our own content to emulate their postings, and track how well it performed with our audience. This is important to us because we want to promote our SNM research to the highest capacity; we want our research to go viral! Below is an example of influencers using the hashtag #Solar!

At Sandia National Labs, we are working alongside some of the most accomplished social-behavioral scientists and computational modelers. This team includes psychologists, sociologists, economists, and modelers working together to create a cause-and-effect model. This work will grow national security efforts; the model suggested several communication options that are most likely to reduce the recruitment and violence of the extremist groups over time. To learn more, click here to see Sandia Labs News Release.