Handling oversampling in dynamic networks using link prediction

Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network given previous snapshots. We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling. We also introduce a novel generative model of noise in dynamic networks that represents oversampling. We demonstrate the results of our approach on both synthetic and real-world data.Comment: ECML/PKDD 201

Dynamic Organizations: Achieving Marketplace and Organizational Agility with People

Driven by dynamic competitive conditions, an increasing number of firms are experimenting with new, and what they hope will be, more dynamic organizational forms. This development has opened up exciting theoretical and empirical venues for students of leadership, business strategy, organizational theory, and the like. One domain that has yet to catch the wave, however, is strategic human resource management (SHRM). In an effort to catch up, we here draw on the dynamic organization (DO) and human resource strategy (HRS) literatures to delineate both a process for uncovering and the key features of a carefully crafted HRS for DOs. The logic is as follows. DOs compete through marketplace agility. Marketplace agility requires that employees at all levels engage in proactive, adaptive, and generative behaviors, bolstered by a supportive mindset. Under the right conditions, the essential mindset and behaviors, although highly dynamic, are fostered by a HRS centered on a relatively small number of dialectical, yet paradoxically stable, guiding principles and anchored in a supportive organizational infrastructure. This line of reasoning, however, rests on a rather modest empirical base and, thus, is offered less as a definitive statement than as a spur for much needed additional research

Crafting A Human Resource Strategy To Foster Organizational Agility: A Case Study

A decade ago, the CEO of Albert Einstein Healthcare Network (AEHN), anticipating a tumultuous and largely unpredictable period in its industry, undertook to convert this organization from one that was basically stable and complacent to one that was agile, “nimble, and change-hardy”. This case study briefly addresses AEHN’s approaches to business strategy and organization design, but focuses primarily on the human resource strategy that emerged over time to foster the successful attainment of organizational agility. Although exploratory, the study suggests a number of lessons for those who are, or will be, studying or trying to create and sustain this promising new organizational paradigm

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings