Centrality Measure
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A Centrality Measure is an quantity that helps with the identification of the most important Network Vertex.
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- Example(s):
- Counter-Example(s):
- See: Network Vertex, Influence, Social Network, Central Point, Graph, Alpha Centrality, Degree Centrality, Betweenness Centrality, Closeness Centrality.
References
2015
- (Wikipedia, 2015) ⇒ http://www.wikiwand.com/en/Centrality
- QUOTE: In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super-spreaders of disease. Centrality concepts were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origin.
- They should not be confused with node influence metrics, which seek to quantify the influence of every node in the network.
2009
- (Macskassy, 2009) ⇒ Sofus A. Macskassy. (2009). “Using Graph-based Metrics with Empirical Risk Minimization to Speed Up Active Learning on Networked Data.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557087
- QUOTE: This paper describes a novel hybrid approach of using of community finding and social network analytic centrality measures to identify good candidates for labeling and then using ERM to find the best instance in this candidate set. We show on real-world data that we can limit the ERM computations to a fraction of instances with comparable performance. (...) For example degree centrality computes the degree of a node: the more nodes you are connected to, the higher your centrality score. Of particular interest here are two particular and often-used metrics known as shortest-path betweenness centrality and shortest-path closeness centrality.