Uncovering Understanding And Predicting Links

"Link Prediction is the problem of inferring new relationships among nodes in a network that can occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account whether the two nodes we are going to link will benefit from that by having an active interaction over time. For instance, it is better to link two nodes ? and ? if we know that these two nodes will interact in the social network in the future, rather than suggesting ?, who may never interact with ?. Thus, the longer the interaction is estimated to last, i.e., persistent interactions, the higher the priority is for connecting the two nodes. This current thesis focuses on the problem of predicting how long two nodes will interact in a network by identifying potential pairs of nodes (?, ?)that are not connected, yet show some Indirect Interaction. "Indirect Interaction" means that there is a particular action involving both the nodes depending on the type of network. For example, in social networks such as Facebook, there are users that are not friends but interact with other user's wall posts. On the Wikipedia hyperlink network, it happens when readers navigate from page ? to page ? through the search box (on the top right corner of page ?), and there is no explicit link on page ? to ?. This research explores cases that involved multiple interactions between ? and ? during an observational time interval [??, ??). Two supervised learning approaches are proposed for the problem. Given a set of network-based predictors, the basic approach consists of learning a binary classifier to predict whether or not an observed Indirect Interaction will last in the future. The second and more fine-grained approach consists of estimating how long the interaction will last by modeling the problem via Survival Analysis or as a Regression task. Once the duration is estimated, this information is leveraged for the Link Prediction task. Experiments were performed on the longitudinal Facebook network and wall interactions dataset, and Wikipedia Clickstream dataset to test this approach of predicting the Duration of Interaction and Link Prediction. Based on the experiments conducted, this study's results show that the fine-grained approach performs the best with an AUROC of 85.4% on Facebook and 77% on Wikipedia for Link Prediction."--Boise State University ScholarWorks.

"Finally, a book to help teachers differentiate math instruction using their own individualized, current data! The practical, simple-to-use formative assessments allow teachers to identify areas of difficulty, correct misconceptions, and guide learning." —Renee Peoples, Fourth Grade Teacher and K–5 District Math Facilitator Swain County Schools, NC "This book offers ways for teachers to gain more insight into what their students know and don′t know." —Carol Amos, Teacher Leader/Mathematics Coordinator Twinfield Union School, VT 25 targeted probes that gauge students′ mathematics comprehension in Grades K–5 Quickly identify each child′s level of understanding with these easy-to-use assessment tools! This sequel to the bestseller Uncovering Student Thinking in Mathematics answers teachers′ requests for more strategies to monitor classroom learning in real time. The authors provide 25 field-tested probes—brief, easily administered assessments—that can pinpoint students′ areas of struggle in mathematics. Aligned with NCTM standards, these grade-appropriate probes are easy to implement immediately and help teachers: Build on children′s current understandings while addressing their identified difficulties Quickly and objectively evaluate specific math skills Determine students′ common mistakes and obstacles to learning math Measure learners′ abilities and compare them to performance objectives Tobey and Minton include their proprietary QUEST cycle model, which provides teachers with the necessary tools to make sound instructional choices and improve all students′ mathematical knowledge.

Terrorist groups throughout the world have been studied primarily through the use of social science methods. However, major advances in IT during the past decade have led to significant new ways of studying terrorist groups, making forecasts, learning models of their behaviour, and shaping policies about their behaviour. Handbook of Computational Approaches to Counterterrorism provides the first in-depth look at how advanced mathematics and modern computing technology is shaping the study of terrorist groups. This book includes contributions from world experts in the field, and presents extensive information on terrorism data sets, new ways of building such data sets in real-time using text analytics, introduces the mathematics and computational approaches to understand terror group behaviour, analyzes terror networks, forecasts terror group behaviour, and shapes policies against terrorist groups. Auxiliary information will be posted on the book’s website. This book targets defence analysts, counter terror analysts, computer scientists, mathematicians, political scientists, psychologists, and researchers from the wide variety of fields engaged in counter-terrorism research. Advanced-level students in computer science, mathematics and social sciences will also find this book useful.

Dive into the engaging universe of library cataloging with this insightful guide. Designed for career professionals, this resource presents an exhaustive exploration of the multifaceted world of cataloging in a friendly, conversational tone. Each chapter weaves intricate narratives, delivering invaluable knowledge about different cataloging roles, required education and training, professional certifications, and essential tips for job interviews and resume building. This guide doesn’t stop at the basics. It prepares professionals for the dynamic nature of cataloging, highlighting emerging trends, the impact of AI and Machine Learning, and the evolving roles in the 21st century. Real-world cataloging project examples provide a practical look into the profession, while further chapters underscore the significance of cataloging in enhancing library usage and patron satisfaction. The journey culminates with an inspiring message for aspiring catalogers, a handy glossary of cataloging terms, a list of essential cataloging tools and resources, and a recommended reading list for deepening your understanding of this intricate field. With a blend of theoretical grounding and practical advice, this is an indispensable resource for those wanting to delve deeper into their profession, seeking knowledge that goes beyond the surface. This guide will empower you to navigate the captivating world of cataloging with confidence, finesse, and a zest for continuous learning.

This book contains innovative research from leading researchers who presented their work at the 17th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2013, held in Kitakyusha, Japan, in September 2013. The conference provided a competitive field of 236 contributors, from which 38 authors expanded their contributions and only 21 published. A plethora of techniques and innovative applications are represented within this volume. The chapters are organized using four themes. These topics include: data mining, knowledge management, advanced information processes and system modelling applications. Each topic contains multiple contributions and many offer case studies or innovative examples. Anyone that wants to work with information repositories or process knowledge should consider reading one or more chapters focused on their technique of choice. They may also benefit from reading other chapters to assess if an alternative technique represents a more suitable approach. This book will benefit anyone already working with Knowledge-Based or Intelligent Information Systems, however is suitable for students and researchers seeking to learn more about modern Artificial Intelligence techniques.

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XI International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2022). The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks and technological networks.

Each nakshatra has some qualities and signification associated with it. Based on this qualities and signification I am going to describe the 100 predictive points.In this book I am going to describe 100 predictive techniques of first 9 nakshatras Ashwini, Bharani Krittika,rohini, mrigashira, ardra, punarvasu, pushya and ashlesha nakshatra. Readers can use this points in their daily predictive with planets placed in it. I hope readers will love it. Regards, Saket Shah

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book