Degree Centrality
Explore degree centrality to measure the prominence of nodes within a network by counting their direct connections. Understand how to apply this concept using Python's NetworkX, visualize centrality with color and size mapping, and identify influential nodes in social or complex networks.
Introduction
Degree centrality is a concept in network analysis that measures the importance or prominence of a node within a graph. In simple terms, it quantifies the number of direct connections a node has with other nodes in the network. A higher degree centrality indicates that a node has more connections, making it more influential or central within the graph.
Degree centrality can be applied to social network analysis. Imagine we are analyzing a social media platform like Twitter, and we want to find out which users are the most influential based on their connections (i.e., followers and followees). In this context, degree centrality can be used to measure the importance of each user in the network. Users with a high degree centrality would have many followers and followees, indicating that they are well-connected and potentially more influential in the network.
Degree centrality can also help identify key players in various ...