This interesting book is about social networks (well beyond the expected Facebook and Twitter) and the many tools from the world of computational intelligence that provide a framework for analyzing and drawing conclusions from them. There are 19 papers, each with relevant references, and an overall index that is helpful but not particularly detailed. Pedrycz and Chen have selected a broad international set of authors that report on a wide range of social network issues and computational intelligence tools. There is a wide variation in the technical depth of the papers, but each is organized in an effective manner for skimming, reading, or studying.

The common view here is that a social network is an architecture linking actors. The study of a social network focuses on detecting patterns, influential entities, and network dynamics. Social networks tend to be complex, self-organizing, and emerging in importance, all of which point to the tools of computational intelligence as the appropriate vehicles for extracting new insights.

To give a readers a sense of the range of social network issues addressed, here is a brief characterization of major topics: detecting community structures; personalization; concept analysis; cyber attacks with state involvement; mobile blogs (moblogs); uncertainty propagation and trust aggregation; social welfare; a visual summary of topic clusters in Twitter; personal intelligence; attention economics; knowledge transfer and sharing; sleepy lizards behavior; the drama process in learning; the product life cycle; tweet cluster analysis; the global spread of Islamism; and economic growth and birth rates. The various topics are quite interesting, and the papers are able to provide helpful context on applications for social networks.

Equally impressive is the range of computational intelligence tools applied to the various networks. These include innovative applications of graph theory; integer programming; adaptive semantics; regression models; fuzzy sets, logic, and models; game theory; genetic algorithms; clustering; agents; sampling; neural networks; swarms; business process modeling; fuzzy c-means; k-nearest neighbor; and hybrids of two or more of the techniques. Some of the algorithms associated with these are explained in some detail, while others assume some familiarity on the part of the reader.

Each paper is essentially self-contained, but the whole is in many ways more than the sum of its parts. This is a rather remarkable collection that is both timely and anticipatory of coming developments and synergy in the application of computational intelligence to social networks. Only a couple of the papers could have been helped by editing to smooth out English usage and grammar, but most are quite well written. I recommend this fascinating volume to practitioners, researchers, and students with an interest in any aspect of applying computational intelligence techniques and tools to extract insights from social networks.