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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.


Papers
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Journal ArticleDOI
TL;DR: It was found that start-ups that have larger informal communication networks increased their chance to survive external shock, and showed that the size of informal interfirm networks really matters.
Abstract: This paper presents new evidence regarding a firm's probability for survival, based on the network structure of the firm's managers. We found that start-ups that have larger informal communication networks increased their chance to survive external shock. Original data have been collected from Israeli software start-ups during the dot-com economic growth. About eight years later, we added information about their ability to survive the burst of the dot-com bubble. From a theoretical point of view, this paper highlights the power of the classic social networks approach in explaining organizational performance. From a practical point of view, these findings offer some guidelines for managers of start-ups. Our results show that the size of informal interfirm networks really matters.

97 citations

01 May 1996
TL;DR: This paper describes the design and implementation of DartFlow, as well as a workflow application that exploits DartFlow''s agent-based design.
Abstract: Workflow management systems help streamline business processes and increase productivity. This paper describes the design and implementation of the DartFlow workflow management system. DartFlow uses Web-browser embedded Java applets as its front end and transportable agents as the backbone. While Java applets provide a safe and platform independent GUI, the use of transportable agents makes DartFlow highly flexible and scalable. This paper describes the design and implementation of DartFlow, as well as a workflow application that exploits DartFlow''s agent-based design.

96 citations

Posted Content
TL;DR: The conclusion of this paper is that the microblogging platform Twitter is Bitcoin's virtual trading floor, emotionally reflecting its trading dynamics.
Abstract: This paper analyzes correlations and causalities between Bitcoin market indicators and Twitter posts containing emotional signals on Bitcoin. Within a timeframe of 104 days (November 23rd 2013 - March 7th 2014), about 160,000 Twitter posts containing "bitcoin" and a positive, negative or uncertainty related term were collected and further analyzed. For instance, the terms "happy", "love", "fun", "good", "bad", "sad" and "unhappy" represent positive and negative emotional signals, while "hope", "fear" and "worry" are considered as indicators of uncertainty. The static (daily) Pearson correlation results show a significant positive correlation between emotional tweets and the close price, trading volume and intraday price spread of Bitcoin. However, a dynamic Granger causality analysis does not confirm a statistically significant effect of emotional Tweets on Bitcoin market values. To the contrary, the analyzed data shows that a higher Bitcoin trading volume Granger causes more signals of uncertainty within a 24 to 72-hour timeframe. This result leads to the interpretation that emotional sentiments rather mirror the market than that they make it predictable. Finally, the conclusion of this paper is that the microblogging platform Twitter is Bitcoin's virtual trading floor, emotionally reflecting its trading dynamics.

91 citations

Journal ArticleDOI
TL;DR: A tool that converts the edit flow among contributors into a temporal social network is developed, using this approach to identify the most creative Wikipedia editors among the few thousand contributors who make most of the edits amid the millions of active Wikipedia editors.

89 citations

Proceedings Article
01 Jan 2008
TL;DR: It is found that discussion patterns on IMDb predict Academy Awards nominations and box office success, and weighting the forum posts of the contributors according to their network position allow for predict trends and real world events in the movie business.
Abstract: This paper introduces a new Web mining approach that combines social network analysis and automatic sentiment analysis. We show how weighting the forum posts of the contributors according to their network position allow us to predict trends and real world events in the movie business. To test our approach we conducted two experiments analyzing online forum discussions on the Internet movie database (IMDb) by examining the correlation of the social network structure with external metrics such as box office revenue and Oscar Awards. We find that discussion patterns on IMDb predict Academy Awards nominations and box office success. Two months before the Oscars were given we were able to correctly predict nine Oscar nominations. We also found that forum contributions correlated with box office success of 20 top grossing movies of 2006.

85 citations


Cited by
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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

14,604 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

Journal ArticleDOI
06 Jun 1986-JAMA
TL;DR: 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 her own research.
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

7,563 citations

Book
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
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.

7,448 citations

Journal ArticleDOI
TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.

4,453 citations