How is community detection different from clustering?
Often clustering and community detection are used interchangeably in the literature. Clustering mostly focuses on a single modality, e.g., using node attributes to group network objects, whereas community detection focuses on network structure as a function of connectivity involving social interaction.
What measures the centrality of the network?
Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman.
Is community detection unsupervised?
One can argue that community detection is similar to clustering. Even though clustering can be applied to networks, it is a broader field in unsupervised machine learning which deals with multiple attribute types.
How is clustering based on a centrality profile?
Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes.
Which is the best measure of centrality in a network?
The red node has maximal degree (greatest number of connections), closeness (is a short distance from other nodes) and betweenness (lies on many shortest-paths between nodes) in this network. In this case these three centrality measures are perfectly concordant.
Are there different types of centrality measures for node roles?
Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear.
Are there real world differences between centrality measures?
However, theoretical and conceptual differences between centrality measures do not always translate into empirical differences in real-world networks. For example, two different centrality measures may behave similarly on real-world networks, thus being practically redundant despite their distinct theoretical foundations.