Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.

Covers the foundations common to the statistical analysis of network data across the disciplines. This book contains topics that include network mapping, characterization of network structure, network sampling, and the modeling, inference, and prediction of networks, network processes, and network flows.

One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.

Until now, practitioners have had access to few detailed descriptions of RTI methods and the effective role they can play in special education. The Handbook of Response to Intervention fills this critical information gap. In this comprehensive volume, more than 90 expert scholars and practitioners provide a guide to the essentials of RTI assessment and identification as well as research-based interventions for improving students’ reading, writing, oral, and math skills.

Designed for both researchers and practitioners, this book is a guide to bridging the gap between the knowledge generated by scientific research and application of that knowledge to educational practice. With the emphasis on evidence-based practice in the schools growing exponentially, school practitioners must learn how to understand, judge, and make use of the research being produced to full effect. Conversely, researchers must understand what is being used in "real-world" settings, and what is still needed. The editors of this book have outlined this process as a series of steps, beginning with being a critical consumer of current research literature, followed by concepts to consider in translating research into practice: systems issues at local, district, and state levels; the role of teachers in program implementation; evaluation of implementation effectiveness, and preservice and inservice professional development of teachers and psychologists. Each chapter is written by leaders on the topic, and contributors include both researchers and school-based practitioners. With contributing authors from a variety of disciplines, this book is an invaluable treatise on current understanding of the complexities of translating research into educational practice.