Social Friend Recommendation Based on Multiple Network Correlation

© 2015 IEEE. Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or 'friend's friends are friends,' which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for network correlation-based social friend recommendation (NC-based SFR). To accomplish this goal, we correlate different 'social role' networks, find their relationships and make friend recommendations. NC-based SFR is characterized by two key components: 1) related networks are aligned by selecting important features from each network, and 2) the network structure should be maximally preserved before and after network alignment. After important feature selection has been made, we recommend friends based on these features. We conduct experiments on the Flickr network, which contains more than ten thousand nodes and over 30 thousand tags covering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods

Survey of User to User Recommendation System in Online Social Networks

The widespread use of online social networks (OSN) and their applications by users lead to the lack of knowledge identification of their needs across the vast amount of data, which made the need to create systems that help people to solve the problems and make decisions with more accuracy, an example of these systems is the Recommendation system (RS), which helps users to make decision and save time in search on a commercial or personal level, one of the most critical types of recommendation systems is the friends recommendation system (FRS) . In this survey, several studies have been suggested to solve the problem of FRS and its mechanism, techniques, and algorithms used to create them Also, the RS types and techniques, a variety of dataset that deals with a specific system, are explained. Moreover, the challenges they face to determine the needs of people in terms of the choice of items or at the level of social networks are included

Middleware Support for Mobile Social Ecosystems

International audienceWith the increased prevalence of advanced mobile devices (the so-called “smart” phones), interest has grown in mobile social ecosystems, where users not only access traditional Web-based social networks using their mobile devices, but are also able to use the context information provided by these devices to further enrich their interactions. Owing to the large variety of platforms available for smart phones, as well as the different ways that data and context information is represented, it is natural to think of middleware solutions that the developers of these systems can use while creating their applications. In this paper, we highlight the issues which should be addressed by middleware designed for mobile social ecosystems, taking into account the heterogeneity of both deployment nodes and available data, the intrinsic distributed nature of mobile social applications, as well as users' security concerns. As part of our ongoing effort to develop this middleware, we present a comprehensive model to represent mobile social ecosystems and the interactions possible in them, and show how to exploit it in a representative scenario

Android Protection System: A Signed Code Security Mechanism for Smartphone Applications

This research develops the Android Protection System (APS), a hardware-implemented application security mechanism on Android smartphones. APS uses a hash-based white-list approach to protect mobile devices from unapproved application execution. Functional testing confirms this implementation allows approved content to execute on the mobile device while blocking unapproved content. Performance benchmarking shows system overhead during application installation increases linearly as the application package size increases. APS presents no noticeable performance degradation during application execution. The security mechanism degrades system performance only during application installation, when users expect delay. APS is implemented within the default Android application installation process. Applications are hashed prior to installation and compared against a white-list of approved content. APS allows applications that generate a matching hash; all others are blocked. APS blocks 100% of unapproved content while allowing 100% of approved content. Performance overhead for APS varies from 100.5% to 112.5% with respect to the default Android application installation process. This research directly supports the efforts of the USAF and the DoD to protect our information and ensure that adversaries do not gain access to our systems