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The emergence of social media and the enormous growth of social networks have initiated a great amount of research in social influence analysis. In this regard, many approaches take into account only structural information while a few have also incorporated content. In this study we propose a new method to rank users according to their topic-sensitive influence which utilizes a priori information by employing supervised random walks. We explore the use of supervision in a PageRank-like random walk while also exploiting textual information from the available content. We perform a set of experiments on Twitter datasets and evaluate our findings.

During the last years, there is increasing interest in analyzing social networks and modeling their dynamics at different scales. This work focuses on predicting the future form of communities, which represent the mesoscale structure of networks, while the communities arise as a result of user interaction. We employ several structural and temporal features to represent communities, along with their past form, that are used to formulate a supervised learning task to predict whether a community will continue as currently is, shrink, grow or completely disappear. To test our methodology, we created a reallife social network dataset consisting of an excerpt of posts from the Mathematics Stack Exchange Q&A site. In the experiments, special care is taken in handling the class imbalance in the dataset and in investigating how the past evolutions of a community affect predictions.

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Advances in wireless communications and networking technologies as well as computer and medical technologies, enable the development of small size, power efficient and more reliable medical multi-parameter recording systems, which can be used for continuous monitoring of patients. Through this paper we present the basic architecture and initial development steps of an m-Health monitoring system that will be used in order to monitor children with suspected cardiac arrhythmias. The proposed system will be based on sensor networks, in order to monitor a subject while being in a predefined area like his/her house; while a module based on PDAs and wearable ECG recorders will be used in order to extent the coverage outside the patient's house. The system will be based on a variable sampling rate to conserve power for the possible arrhythmia episode. The system design has been completed, the hardware specifications have been decided and currently the system is going through the development phase.