9 research outputs found

    A Review of Several Privacy Violation Measures for Large Networks under Active Attacks

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    It is by now a standard practice to use the concepts and terminologies of network science to analyze social networks of interconnections between people such as Facebook, Twitter and LinkedIn. The powers and implications of such social network analysis are indeed indisputable; for example, such analysis may uncover previously unknown knowledge on community-based involvements, media usages and individual engagements. However, all these benefits are not necessarily cost-free since a malicious individual could compromise privacy of users of these social networks for harmful purposes that may result in the disclosure of sensitive data that may be linked to its users. A natural way to avoid this consists of an “anonymization process” of the relevant social network. However, since such anonymization processes may not always succeed, an important research goal is to quantify and measure how much privacy a given social network can achieve. Toward this goal, some recent research works have aimed at evaluating the resistance of a social network against active privacy-violating attacks by introducing and studying a new and meaningful privacy measure for social networks. In this chapter, we review both theoretical and empirical aspects of such privacy violation measures of large networks under active attacks

    Some Perspectives on Network Modeling in Therapeutic Target Prediction

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    Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities

    Geodesic-based Properties in Complex Networks

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    In the modern age of the Internet, complex large-scale networks are most likely a part of our daily interactions. From social networks like Facebook and Twitter, to technological systems such as World Wide Web and the Internet, to cell structure and traffic routes, a wide variety of systems can be described by complex networks, making their impact and relevance indisputable. To understand the behavior and performance of complicated systems as a whole, we need to expand our understanding of the topology and structure of underlying network, which makes the investigation of network measures that reflect the most salient properties of complex large-scale networks a high demand area in the network science community. This thesis, investigates multiple geodesic-based properties in complex networks, which provide beneficial insight to network behavior and performance. We adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parameterized finite networks and show that a variety of biological and social networks are hyperbolic. We also look into the complexity of a geodesic-based property known as strong metric dimension and show the approximation and inapproximability results for the problem of calculating the strong metric dimension of a graph with n nodes. Finally, we investigate a geodesic-based property that indicates the privacy violation in large–networks under active attacks. Our result provides some insight regarding prevention of privacy violation and designing topology of networks, as well as shedding light on privacy violation properties of real social networks and a large number of synthetic networks