Potential Indirect Relationships in Productive Networks

Productive Networks, such as Social Networks Services, organize evidence about human behavior. This evidence is independent of the network content type, and may support the discovery of new relationships between users and content, or with other users. These indirect relationships are important for recommendation systems, and systems where potential relationships between users and content (e.g., locations) is relevant, such as with the emergency management domain, where the discovery of relationships between users and locations on productive networks may enable the identification of population density variations, increasing the accuracy of emergency alerts. This thesis presents a Productive Networks model, which enables the development of a methodology for indirect relationships discovery, using the metadata on the network, and avoiding the computational cost of content analysis. We designed and conducted a set of experiments to evaluate our proposals. Our results are twofold: firstly, the productive network model is sufficiently robust to represent a wide range of networks; secondly, the indirect relationship discovery methodology successfully identifies relevant relationships between users and content. We also present applications of the model and methodology in several contexts

Learning relationships from theory to design

This paper attempts to bridge the psychological and anthropological views of situated learning by focusing on the concept of a learning relationship, and by exploiting this concept in our framework for the design of learning technology. We employ Wenger's (1998) concept of communities of practice to give emphasis to social identification as a central aspect of learning, which should crucially influence our thinking about the design of learning environments. We describe learning relationships in terms of form (one‐to‐one, one‐to‐many etc.), nature (explorative, formative and comparative), distance (first‐, second‐order), and context, and we describe a first attempt at an empirical approach to their identification and measurement

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Implementation of an experimental platform for the social internet of things

The convergence of the Internet of Things (IoT) technologies with the social networking concepts has led to a new paradigm called the Social Internet of Things (SIoT), where the objects mimic the human behavior and create their own relationships based on the rules set by their owner. This is aimed at simplifying the complexity in handling the communications between billions of objects to the benefits of the humans. Whereas several IoT platforms are already available, the SIoT paradigm has represented only a field for pure research and simulations, until now. The aim of this paper is to present our implementation of a SIoT platform. We begin by analyzing the major IoT implementations, pointing out their common characteristics that could be re-used for our goal. We then discuss the major extensions we had to introduce on the existing platforms to introduce the functionalities of the SIoT. We also present the major functionalities of the proposed system: how to register a new social object to the platform, how the system manages the creation of new relationships, and how the devices create groups of members with similar characteristics. We conclude with the description of possible simple application scenarios

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur