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Community search over big graphs
Huang X., Lakshmanan L., Xu J., Morgan&Claypool Publishers, San Rafael, CA, 2019. 208 pp.  Type: Book (978-1-681735-95-5)
Date Reviewed: Dec 19 2019

Community structures exist normally in numerous real-world networks. Social, biological, and communication networks are some examples of community structures. In the first chapter of the book, the authors discuss some applications for community search as well as the outline of the book. The second chapter provides the required graph-theoretical notions. All real-world communities have many types of structural characteristics; hence, community search problems demand high efficiency in modeling the problems. As a result, the content of the paper focuses on community models based on cohesive/dense subgraphs.

In chapter 3, the authors discuss community search problems and simple graphs, with a prime focus on the structural characteristics of networks. They define community as a densely connected subgraph and then describe two types of community models, clique based and core based, and some community detection/search algorithms. Following these discussions, different types of influential community models and truss-based community models, and the corresponding community search algorithms, are also discussed. The final part of the chapter covers query-biased densest community models. Chapter 4 discusses the corresponding types of attributed community networks and community search.

The fifth chapter presents the structural characteristics of communities, called social circles. Initially, the authors introduce the concept of ego networks and then describe structural diversity search, its problem formulation, and a degree-based algorithmic approach to the problem. Finding all social circles in the user’s ego networks follows. The corresponding problem formulation and a generative model for social circle discovery are also discussed here.

Geosocial community search and its different models are explained in chapter 6. The authors then discuss the corresponding problem formulations and search algorithms in this area. The seventh chapter discusses real-world datasets to validate the practices, followed by different community models, evaluation metrics, and software demo systems. In view of the theoretical analysis and experimental results, the authors propose some ways to select dense subgraphs for community models at the end of the chapter. The last chapter (8) suggests some interesting problems for further investigation.

The content of the book is so impressive and interesting because it presents real-life problems and the corresponding graph-theoretical modeling in an effective and efficient way. The included illustrations and algorithms help with reading and understanding. The authors cover this interesting, relevant, and significant topic in an effective and efficient manner.

Reviewer:  Sudev Naduvath Review #: CR146817 (2003-0045)
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Sorting/ Searching (E.5 ... )
 
 
Algorithm Design And Analysis (G.4 ... )
 
 
Graph Theory (G.2.2 )
 
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