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Centrality and diversity in search : roles in A.I., machine learning, social networks, and pattern recognition
Murty M., Biswas A., Springer International Publishing, New York, NY, 2019. 94 pp.  Type: Book (978-3-030247-12-6)
Date Reviewed: Dec 10 2019

This book is part of the “SpringerBriefs in Intelligent Systems” series, which covers diverse areas of intelligent systems, including artificial intelligence (AI), multiagent systems, and cognitive robotics. From their web profiles I gather that Dr. Murty is a professor at the Indian Institute of Science and Biswas is a graduate student at the same institution. I could not verify their levels of expertise from independent sources, but from their profiles it is clear that they are well informed in the areas covered.

I found the title of the book very attractive; therefore, I volunteered to review it. As an engineer and researcher working in this and related areas, I imagined that I could extract some practical value by reviewing this book, especially in the area of machine learning. However, I found the content to be of very little practical value to me and engineers like me. It may have some value for beginners, eager to be exposed to many known techniques.

The chapters of the book, after a one-page preface, are organized as follows.

Following a one-page preface, the introductory chapter includes basic concepts of representation, clustering and classification, ranking, regression, and social networks and recommendation systems. These well-known topics are aggregated under the title of “Centrality and Diversity.” The chapter ends with a one-paragraph summary and a five-book reference.

The second chapter, “Searching,” has two subsections: “Introduction” covers concepts such as exact match, inexact match, and representation; “Proximity” reviews the well-known notions of distance function, clustering, classification, information retrieval, and problem-solving in AI. The chapter’s brief summary maps tasks to “centrality and diversity.” I do not understand the importance of this table. Five books are given as references.

Chapter 3, “Representation,” summarizes known mechanisms such as vector space representation, representing text documents, representing a cluster, and representing classes and classifiers. The chapter summary includes three tables subjectively classifying notions in terms of “centrality and diversity.” The bibliography includes five books and three papers.

Chapter 4 proceeds with the same style, reviewing well-known concepts related to clustering-based matrix factorization, feature selection, principal component analysis, singular value decomposition, diversity, clustering, and classification. The chapter ends with a short summary and ten references.

Chapter 5 is only a few pages and should not be labeled as a chapter. Titled “Ranking,” it summarizes ranking based on similarity and density. References include six sources.

Chapter 6, “Centrality and Diversity in Social and Information Networks,” seems to be an original application of the notions discussed in the book. The chapter ends with a short summary and nine references.

Chapter 7 is only one page and should not be called a chapter. It is an overview of the preceding chapters.

In conclusion, this book will not be useful to experienced readers in the area. It is also hard for beginners to follow due to the cryptic nature of the short reviews. I suspect that it will be useful to readers who need a general overview of the subjects.

Reviewer:  M. M. Tanik Review #: CR146809 (2002-0013)
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Sorting/ Searching (E.5 ... )
Clustering (H.3.3 ... )
Learning (I.2.6 )
Data Structures (E.1 )
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