Organizational Information Dissemination Within Collaborative Networks Using Digital Communication Tools

Knowledge transfer among employees remains a challenge for many organizations. With the increased adoption rate of corporate social media collaboration technologies, there is an urgent need to determine the factors that enhance information diffusion among employees. \ \ The current study leverages prior research on social media collaboration performed in the public domain to determine whether the dimensions of tie strength have any effect on information diffusion among corporate users of social media technologies. Analysis of 511 Lync messages related to a training topic at a mid-sized South Central corporation was performed. The preliminary results demonstrated that the dimensions of tie strength (intimacy, structural, and social) were highly correlated to the number of messages among users. Moreover, regression analysis showed that three quarters of the messages were also predicted by the dimensions of tie strength.

Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory. The discussion will be organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.Comment: 107 pages, 25 figure

A survey of statistical network models

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.Comment: 96 pages, 14 figures, 333 reference

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections

Cross-Promotion in Social Media: Choosing the Right Allies

This paper investigates the strategic use of cross-promotion for content producers in social media. In particular, we study how a producer chooses other producers to cross-promote so as to maximize the expected benefits of them cross-promoting him/her in return. Theories on homophily effect and social influence suggest that cross-promoted producers are more likely to cross-promote the initiator in return when they are in the similar categories or share more common friends and when the initiator has higher status. However, the cross-promotion from producers of different categories and social groups (i.e., share fewer common friends) tend to benefit the initiator more. The benefits also increase as the status of the initiator increases. We collected a panel of data consisting of 27,356 producers’ profile and status information, content categories, and their cross-promotion activities over a period of two months from YouTube. To test our hypotheses, we first employ a cox proportional hazard model to estimate the probability of cross-promotion in return. Then, we use a difference-in-differences method with panel fixed effects to evaluate the effect of cross-promotion in return on the initiator. Our results strongly support our hypotheses and provide valuable insights for both content producers and social media platforms