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One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks, the propagation of information from a node to another may incur a certain amount of time delay; moreover, the value of information may decrease over time. So not only the coverage size, but also the propagation speed matters. In this paper, we propose the Time-Sensitive Influence Maximization (TSIM) problem, which takes into account the time dependence of the information value. Considering the time delay aspect, we develop two diffusion models, namely the Delayed Independent Cascade model and the Delayed Linear Threshold model. We show that the TSIM problem is NP-hard under these models but the spread function is monotone and submodular. Thus, a greedy approximation algorithm can achieve a 1 − 1/e approximation ratio. Moreover, we propose two time-sensitive centrality measures and compare their performance with the greedy algorithm. We evaluate our methods on four real-world datasets. Experimental results show that the proposed algorithms outperform existing methods, which ignore the decay of information value over time. [ABSTRACT FROM AUTHOR]

This paper presents a study on factors that may increase the risks of personal information leakage, owing to the possibility of connecting user profiles that are not explicitly linked together. First, we introduce a technique for user identification based on cross-site checking and linking of user attributes. Then, we describe the experimental evaluation of the identification technique both in a real setting and on an online sample, showing its accuracy to discover unknown personal data. Finally, we combine the results on the accuracy of identification with the results of a questionnaire completed by the same subjects who performed the test in the real setting. The aim of the study was to discover possible factors that make users vulnerable to this kind of technique. We found that the number of social networks used, their features and especially the amount of profiles abandoned and forgotten by the user are factors that increase the likelihood of identification and the privacy risks. [ABSTRACT FROM PUBLISHER]