In rural Trinidad a teenager with second sight sees creatures invisible to his siblings. Misunderstood and feared, Devon Alexander, goes through several misfortunes in life. After being separated from his siblings at age ten, his life makes a sudden change with the return of his childhood friend Malik who eventually leads him to living a well-financed and comfortable life. In returns Allister, his elder but troubled brother, who supports Devon to the reality that his long lost father may be alive. Through the sudden introduction of a beautiful, white faced entity, Devon learns that he is no different from the creatures he feared – one of a long line of entities that walk the earth, tracked and monitored by forbidden minions’ intent on tainting his kind... willing to do anything to get their way.
When Theo discovers the father he thought died when he was a baby is still alive, he's determined to find him. The clues lead him to lonely Rachel, who has problems of her own, including parents who compare her unfavourably to her long-dead sister. But when Rachel and Theo are attacked by men from RAGE - the Righteous Army against Genetic Engineering - they are rescued by strangers and taken to meet a mysterious figure who leads them to make startling discoveries about their identities, which will affect their past, present, and future in dramatic and life-altering ways...
Understanding Criminal Networks is a short methodological primer for those interested in studying illicit, deviant, covert, or criminal networks using social network analysis (SNA). Accessibly written by Gisela Bichler, a leading expert in SNA for dark networks, the book is chock-full of graphics, checklists, software tips, step-by-step guidance, and straightforward advice. Covering all the essentials, each chapter highlights three themes: the theoretical basis of networked criminology, methodological issues and useful analytic tools, and producing professional analysis. Unlike any other book on the market, the book combines conceptual and empirical work with advice on designing networking studies, collecting data, and analysis. Relevant, practical, theoretical, and methodologically innovative, Understanding Criminal Networks promises to jumpstart readers’ understanding of how to cross over from conventional investigations of crime to the study of criminal networks.
Today, 70 percent of the American public supports reforms that would limit the number of terms a state legislator may serve, and the advocacy group U.S. Term Limits promotes this reform at all levels of government. But are advocates correct that term limits ensure citizens dedicated to the common good—rather than self-serving career politicians—run government? Or does the enforced high rate of turnover undermine the legislature’s ability to function? In Implementing Term Limits, Marjorie Sarbaugh-Thompson and Lyke Thompson bring thirteen years of intensive research and 460 interviews to assess changes since Michigan’s implementation of term limits in 1993 and explore their implications. Paying special attention to term limits’ institutional effects, they also consider legislative representation, political accountability, and the role of the bureaucracy and interest groups in state legislatures. Their thorough study suggests that legislators are less accessible to officials and that there is a larger gap between legislators and their voters. Moreover, legislators become much more politically ambitious after term limits and spend more time on political activities. The selection of top chamber leaders is complicated by newcomers’ lack of knowledge about and experience working with the leaders they elect before being sworn in. As a result, term limits in Michigan fail to deliver on many of the “good government” promises that appeal to citizens. Implementing Term Limits makes a unique and valuable contribution to the debate over the best means by which to obtain truly democratic institutions.
Online social networks have already become a bridge connecting our physical daily life with the (web-based) information space. This connection produces a huge volume of data, not only about the information itself, but also about user behavior. The ubiquity of the social Web and the wealth of social data offer us unprecedented opportunities for studying the interaction patterns among users so as to understand the dynamic mechanisms underlying different networks, something that was previously difficult to explore due to the lack of available data. In this book, we present the architecture of the research for social network mining, from a microscopic point of view. We focus on investigating several key issues in social networks. Specifically, we begin with analytics of social interactions between users. The first kinds of questions we try to answer are: What are the fundamental factors that form the different categories of social ties? How have reciprocal relationships been developed from parasocial relationships? How do connected users further form groups? Another theme addressed in this book is the study of social influence. Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. Considerable research has been conducted to verify the existence of social influence in various networks. However, few literature studies address how to quantify the strength of influence between users from different aspects. In Chapter 4 and in [138], we have studied how to model and predict user behaviors. One fundamental problem is distinguishing the effects of different social factors such as social influence, homophily, and individual's characteristics. We introduce a probabilistic model to address this problem. Finally, we use an academic social network, ArnetMiner, as an example to demonstrate how we apply the introduced technologies for mining real social networks. In this system, we try to mine knowledge from both the informative (publication) network and the social (collaboration) network, and to understand the interaction mechanisms between the two networks. The system has been in operation since 2006 and has already attracted millions of users from more than 220 countries/regions.
This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way. The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it: • Discusses measures and techniques for analyzing social network data, including digital media • Explains a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks • Offers digital resources like practice datasets and worked examples that help you get to grips with R software
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling. Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more. Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
