R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot. Social Network Analysis (SNA) is useful in real-world settings because it focuses on relationships, not just individual characteristics. Instead of asking “Who is important?” based on a title or role, SNA asks “Who is connected to whom?” and “How do those connections shape influence, trust, and information flow?” By mapping and measuring ties between people or organizations, analysts can identify key actors, hidden groups, bottlenecks, and vulnerabilities that would not be visible in a spreadsheet or organizational chart.

One major area where SNA is valuable is criminal justice and public safety. Law enforcement agencies can analyze co-offending networks, gang affiliations, trafficking operations, or fraud rings. For example, in a drug trafficking investigation, officers might map communication patterns between suspects. Degree centrality could identify individuals with many direct connections (potential coordinators), while betweenness centrality could highlight people who act as bridges between otherwise separate groups. Those “bridges” may be critical for moving money or drugs across different regions. Targeting them could disrupt the network more effectively than focusing only on the most visible members.

SNA is also useful in business and organizational settings. Companies can analyze internal communication networks to understand collaboration patterns. Sometimes, the most influential employee is not a manager but someone who connects multiple departments. Identifying those informal connectors can help leadership improve workflow, reduce silos, and strengthen team coordination. Community detection (cluster analysis) can reveal subgroups that naturally form within the organization—such as departments, project teams, or informal social circles.

Subgroup or community-level analysis is especially important in most real-world settings. Networks are rarely uniform; they usually contain clusters of people who interact more frequently with each other than with the rest of the system. Understanding these clusters helps analysts see internal structure. In a criminal network, subgroups might represent different roles (suppliers, distributors, recruiters). In a company, clusters might reveal innovation teams or isolated departments that need better integration. Without community analysis, we might miss how influence and behavior spread within specific pockets of the network.

For an original example, imagine analyzing a high school’s social network to prevent bullying. Students could be represented as nodes, and friendships or regular interactions as edges. SNA could identify tightly connected friend groups (communities), socially isolated students, and highly central students who connect multiple groups. If bullying incidents occur, administrators could examine whether certain central individuals are influencing group behavior. Instead of punishing random individuals, interventions could focus on key influencers who shape norms across multiple groups. Community-level analysis would be critical here because bullying dynamics often operate within specific social circles rather than across the entire school.

In short, Social Network Analysis helps move from looking at individuals in isolation to understanding systems of relationships. Whether analyzing criminal organizations, corporate teams, online communities, or school social structures, SNA provides insight into influence, cohesion, vulnerability, and group dynamics. Subgroup analysis is often essential because real-world networks are structured in clusters, and those clusters shape how information, behavior, and power move through the system.