Peter A. Gloor

Bio: Peter A. Gloor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Social network. The author has an hindex of 37, co-authored 211 publications receiving 4918 citations. Previous affiliations of Peter A. Gloor include University of Cologne & Union Bank of Switzerland.

GalaxyScope: Finding the “Truth of Tribes” on Social Media

Abstract: This paper introduces GalaxyScope, a novel system to distinguish different interpretations of “truth” for different virtual tribes. It extracts the tribes from Wikipedia through analyzing its categories “Ideologies”, “Lifestyles”, and “Culture”, leading to tribes such as “capitalism”, “socialism”, and “liberalism”. It then calculates the most influential “tribe leaders” through their association on Wikipedia with these concepts. To score their influence in Wikipedia, we use a novel metric we call “reach2” which measures how many people somebody can reach within two degrees of separation on Wikipedia living people pages. It subsequently calculates the vocabulary on Twitter of the tribe leaders, and uses these words to automatically assign individuals to tribes, as well as calculating the relevance of text documents such as tweets or news items for each tribe.

Cybermap—Visually Navigating the Web

Abstract: The general problem we address in this paper is the automatic generation of structural overview maps of large collections of unstructured data to aid in the navigation of collections such as the Web and data retrieved from search engines. We present the theoretical foundations of such a generator, CyberMap, as well as the algorithms used for its implementation. We will detail a series of actual prototype implementations of CyberMap in HyperCard on the Macintosh, in C*on the massively parallel Connection Machine CM-2, as well as in C and Java for the World Wide Web.

Using Collaborative Improvement and Innovation Networks to Tackle Complex Population Health Problems.

Abstract: In medicine and public health, we often confront highly complex, persistent challenges that have both multiple causes and potential levers for action. In pediatrics, this is particularly true, as promoting children’s optimal health requires an attention to life course—outcomes decades removed from intervention—as well as to issues within and beyond health care such as housing, education, and safety. If we are to make headway on such challenges, we must be open to approaches that might facilitate success in novel ways. Collaborative Improvement and Innovation Networks (CoIINs) hold promise for public health and health professionals working to address complex problems related to children’s health by combining distinctive features of 3 key methods for change— collaborative learning, collaborative innovation networks, and quality improvement (QI). After reviewing the CoIIN model, we describe its key elements based on the CoIIN to Reduce Infant Mortality, or IM CoIIN, an initiative led by HRSA and the National Institute for Children’s Health Quality that illustrates how a CoIIN can be implemented to achieve results in a population health

Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research

Abstract: With the blessing of information technology, we are living in an increasingly networked world. People, information and other entities are connected via World Wide Web, email networks, instant messaging networks, mobile communication networks, online social networks, etc. These online networks grow fast and possess huge amount of recorded information, which presents great opportunities in understanding the science of these networks, and in developing new applications from these networks and for these networks. The increasingly networked society has fundamentally changed our way of thinking, individual behaviors and social activities. It is foreseen that the public health relating to epidemic diseases is greatly impacted by this emerging connectivity as they are by nature mediated by direct or indirect human interactions and mobility. However, new challenges have to be met --- the networks are huge and information is noisy, and they demand new methodologies in accessing and analyzing these networks, and in developing theories and applications for the networks. To meet with these challenges, researchers from a wide range of academic fields, including theory and algorithms, data mining and machine learning, computer systems and networks, statistical physics and complex systems, sociology, social psychology, economics and managerial science, etc. are all actively studying various aspects concerning social and information networks. However, we lack the proper opportunities for people from these diverse backgrounds to directly interact with each other. The diversity of approaches and methodologies to study various social networks has raised the need for an interdisciplinary effort to create the required expertise to address the fundamental open questions in this field. This workshop is intended to present such an opportunity and serve as a forum to bring together people from various fields to exchange their latest research results and to sparkle new ideas and directions to properly understand these networks. It will be held on August 12, 2012, in conjunction with ACM KDD 2012, August 12-16, Beijing, China.

Using Multivariate Statistics

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Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.