Degree Centrality in Social Network Analysis

Degree Centrality in Social Network Analysis

🔹 What Is Degree Centrality?
Degree centrality is one of the simplest and most commonly used measures in network analysis. It refers to the number of connections that a given node (e.g., a person, institution, website) has within the structure of the entire network. Simply put, it is the number of neighbors that a particular element is directly connected to. In the context of social networks, this may indicate the number of people an individual is in contact with or interacts with.

In undirected networks (where relationships are mutual—like friendship), a node’s degree centrality is simply the number of lines (edges) coming from it. In directed networks (e.g., "A follows B" on Twitter), we measure separately:

• In-degree – the number of edges coming into the node (e.g., how many users follow a person)

• Out-degree – the number of edges going out of the node (e.g., how many users a person follows)

This measure allows us to quickly identify the most active or most “visible” elements in a network.

🔹 Example Network Diagram
To better understand the concept of degree centrality, it's helpful to use a simple visual example. Let’s consider a network with five nodes labeled A, B, C, D, and E. The relationships between them can be shown as follows:

In this example:

• Node A is connected to three other nodes: B, C, and D, so its degree centrality is 3.

• Nodes B, C, and E each have only one connection – meaning their degree centrality is 1.

• Node D has two connections – with A and E – so its degree centrality is 2.

From this simple example, we can see that node A is at the center of the local structure – it has the most direct relationships. Therefore, it potentially plays a greater role in the flow of information or influence within the network.

🔹 The Meaning and Interpretation of Degree Centrality
Degree centrality tells us how involved a node is in the network — in other words, how many direct contacts it has. In the context of social networks, this can be interpreted as:

• Popularity – individuals with high degree centrality have many connections, which may indicate sociability or importance in the group.

• Access to information – central individuals receive messages more quickly and can disseminate them more easily.

• Influence and position – central people often act as leaders or intermediaries in communication.

In organizational networks (e.g., in companies), degree centrality can help identify individuals crucial to team operations, as they maintain many contacts and can connect different departments. In web networks (e.g., the WWW), pages with high degree centrality may have more traffic because they are frequently linked by other sites.

However, it’s worth noting that the number of connections alone doesn’t always reflect true influence — sometimes, what matters more is a node’s strategic position or ability to connect distant parts of the network (measured by other indicators like betweenness centrality).

🔹 Conclusion
Degree centrality is an intuitive and easy-to-calculate metric that helps identify the most active and best-connected elements in a network. While it doesn’t tell the whole story about a node’s role, it provides fundamental information about its visibility and potential importance.

In practice:

• In social media, it indicates accounts with the most friends/followers.

• In organizational network analysis, it helps identify employees with key professional relationships.

• In marketing, it supports the identification of influencers — individuals with a large range of influence.

With the help of tools such as Pajek, degree centrality can be easily calculated for each node and visualized graphically, making network data analysis and interpretation significantly easier.