In this section, you can explore four different network centrality measures. Use the selectors at the top to choose the metric you're interested in, and the network will highlight the node with the highest value for that metric. Additionally, you can hover over each node to view its respective values across all four centrality measures.
On the right, you’ll find explanations of the social actors with the highest metrics in the network. At the bottom, you can also review the complete table of results for all four metrics for each social actor in the network.
(This interactive function was developed by Nethabitus)
Degree
Is a count of the number of unique edges that are connected to it. Fay has a degree metric of 6 because this user is directly connected to 6 other people.
Betweenness
It helps identify people who play a "bridge" role in a network. Ava has a high betweenness centrality (38).
Closeness
How close each person is to the other people in the network. Ethan has the highest closeness centrality because this user is positioned right in the "middle" of the network (0.05).
Eigenvector
It takes into account the connections that a node has and the centrality of the nodes with which it is connected. Gabe has the highest eigenvector (0.169) because his degree metric is relatively high (5), and also because those he connects with have high Eigenvector centrality scores.
Understanding Indegree and Outdegree in Networks
Indegree and Outdegree are key concepts in network analysis, especially in the context of directed graphs, where connections (links) have a specific direction.
The analysis of Indegree and Outdegree provides crucial information about the structure and dynamics of a network. Indegree is more associated with receiving and measuring popularity, while Outdegree relates to spreading and measuring influence capacity. These concepts are fundamental in network theory, as they help identify key nodes in various contexts, from social networks to communication systems, biology, and beyond.
About the visualization: In this visualization, you can explore how nodes interact within a network, distinguishing those that act as major receivers (with high Indegree) from those that serve as key transmitters (with high Outdegree). This helps in understanding the structure, influence, and flow of information in the network.
(This interactive function was developed by Nethabitus)
Summary of main centrality measures
Recommended readings
(Open Access)
By. Dr. Verónica Espinoza
Network science
Barabási, A.-L. (n.d.). Network science. Retrieved October 9, 2024, from https://networksciencebook.com/chapter/3#networks-supercritical
[Link]
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