The concepts of centrality and diversity are highly important in
search algorithms, and play central roles in applications of artificial
intelligence (AI), machine learning (ML), social networks, and pattern
recognition. This work examines the significance of centrality and
diversity in representation, regression, ranking, clustering,
optimization, and classification.
The text is designed to be accessible to a broad readership. Requiring
only a basic background in undergraduate-level mathematics, the work is
suitable for senior undergraduate and graduate students, as well as
researchers working in machine learning, data mining, social networks,
and pattern recognition.