From online social network analysis to a user-centric private sharing system

Online social networks (OSNs) have become a massive repository of data constructed from individuals’ inputs: posts, photos, feedbacks, locations, etc. By analyzing such data, meaningful knowledge is generated that can affect individuals’ beliefs, desires, happiness and choices—a data circulation started from individuals and ended in individuals! The OSN owners, as the one authority having full control over the stored data, make the data available for research, advertisement and other purposes. However, the individuals are missed in this circle while they generate the data and shape the OSN structure. In this thesis, we started by introducing approximation algorithms for finding the most influential individuals in a social graph and modeling the spread of information. To do so, we considered the communities of individuals that are shaped in a social graph. The social graph is extracted from the data stored and controlled centrally, which can cause privacy breaches and lead to individuals’ concerns. Therefore, we introduced UPSS: the user-centric private sharing system, in which the individuals are considered as the real data owners and provides secure and private data sharing on untrusted servers. The UPSS’s public API allows the application developers to implement applications as diverse as OSNs, document redaction systems with integrity properties, censorship-resistant systems, health care auditing systems, distributed version control systems with flexible access controls and a filesystem in userspace. Accessing users’ data is possible only with explicit user consent. We implemented the two later cases to show the applicability of UPSS. Supporting different storage models by UPSS enables us to have a local, remote and global filesystem in userspace with one unique core filesystem implementation and having it mounted with different block stores. By designing and implementing UPSS, we show that security and privacy can be addressed at the same time in the systems that need selective, secure and collaborative information sharing without requiring complete trust

INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

With the proliferation of graph applications in social network analysis, biological networks, WWW and many other areas, a great demand of efficient and scalable algorithms for graph mining is rising. In many applications, finding the most influential nodes in the network is informative for the network analyzers in order to track the spread of information, disease and rumors. The problem of finding the top k influential nodes of a directed graph G=(V,E) such that the influence spread of these nodes will be maximized has long been exposed and many algorithms have been proposed to deal with this problem. Despite the useful characteristics of community structure in social networks, only a few works have studied the role of communities in the spread of influence in social networks. In this paper we propose an efficient algorithm (which has an acceptable response time even for large graphs) for finding the influential nodes in the graph under linear threshold model. We exploit the community structure of graph to find the influential communities, and then find the influence of each node as a combination of its local and global influences. We compare our algorithm with the state-of-the-art methods for influence maximization problem and the results of our experiments on real world datasets show that our approach outperforms the other ones in the quality of outputted influential nodes while still has acceptable running time and memory usage for large graphs

Time sensitive blog retrieval using temporal properties of queries

Blogs are one of the main user-generated contents on the web and are growing in number rapidly. The characteristics of blogs require the development of specialized search methods which are tuned for the blogosphere. In this paper, we focus on blog retrieval, which aims at ranking blogs with respect to their recurrent relevance to a user\u27s topic. Although different blog retrieval algorithms have already been proposed, few of them have considered temporal properties of the input queries. Therefore, we propose an efficient approach to improving relevant blog retrieval using temporal property of queries. First, time sensitivity of each query is automatically computed for different time intervals based on an initially retrieved set of relevant posts. Then a temporal score is calculated for each blog and finally all blogs are ranked based on their temporal and content relevancy with regard to the input query. Experimental analysis and comparison of the proposed method are carried out using a standard dataset with 45 diverse queries. Our experimental results demonstrate that, using different measurement criteria, our proposed method outperforms other blog retrieval methods

Replication Data for: Classification of sleep, sedentary behaviour, and physical activity using commercial wearable devices

This dataset is the replication data for the paper "Classification of sleep, sedentary behaviour, and physical activity using commercial wearable devices". It includes the data collected at 1Hz heart rate, steps, distance, and calories from Apple Watch and Fitbit. We also collected participants age, self-report height and weight. We created physical activity and sleep labels using the data collected by GENEActiv device. Each dataset includes 57097 and 21489 minutes of data for Apple Watch and Fitbit, respectively. The heart rate attribute for both datasets is interpolated using linear interpolation. Attribute Information: 1- Heartrate (bpm), 2- Calories (kcal), 3- Steps (count), 4- Distance (km), 5- Age (numerical), 6- Gender (M/F), 7- Weight (lb), 8- Height (ft), 9- Activity (Sleep/Sedentary/Light/Moderate/Vigorous) Implementation: Is available at BEAP Lab GitHub page here</p