Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach

We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm's memory are removed after the stream is finished. We develop a robust submodular algorithm STAR-T. It is based on a novel partitioning structure and an exponentially decreasing thresholding rule. STAR-T makes one pass over the data and retains a short but robust summary. We show that after the removal of any m elements from the obtained summary, a simple greedy algorithm STAR-T-GREEDY that runs on the remaining elements achieves a constant-factor approximation guarantee. In two different data summarization tasks, we demonstrate that it matches or outperforms existing greedy and streaming methods, even if they are allowed the benefit of knowing the removed subset in advance.Comment: To appear in NIPS 201

Social Recommendation Systems

In recent years, with the rise of online social networks, personalized recommendations that leverage the aspect of social connections have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. In this research, we model the decay in influence propagation in directed graphs, utilizing the structural properties of the social graph to measure the propagated influence beyond one-hop. This social network information from influence propagation is also combined with matrix factorization as a social regularization factor. We then employ this unified framework to form social recommendations, and present our experimental results using real-life datasets. Extensive experimental analysis demonstrate that our proposed methodology outperforms state-of-the-art techniques for generating social recommendations

Analysis of Layered Social Networks

Prevention of near-term terrorist attacks requires an understanding of current terrorist organizations to include their composition, the actors involved, and how they operate to achieve their objectives. To aid this understanding, operations research, sociological, and behavioral theory relevant to the study of social networks are applied, thereby providing theoretical foundations for new methodologies to analyze non-cooperative organizations, defined as those trying to hide their structure or are unwilling to provide information regarding their operations. Techniques applying information regarding multiple dimensions of interpersonal relationships, inferring from them the strengths of interpersonal ties, are explored. A layered network construct is offered that provides new analytic opportunities and insights generally unaccounted for in traditional social network analyses. These provide decision makers improved courses of action designed to impute influence upon an adversarial network, thereby achieving a desired influence, perception, or outcome to one or more actors within the target network. This knowledge may also be used to identify key individuals, relationships, and organizational practices. Subsequently, such analysis may lead to the identification of exploitable weaknesses to either eliminate the network as a whole, cause it to become operationally ineffective, or influence it to directly or indirectly support National Security Strategy

The Behavior of Epidemics under Bounded Susceptibility

We investigate the sensitivity of epidemic behavior to a bounded susceptibility constraint -- susceptible nodes are infected by their neighbors via the regular SI/SIS dynamics, but subject to a cap on the infection rate. Such a constraint is motivated by modern social networks, wherein messages are broadcast to all neighbors, but attention spans are limited. Bounded susceptibility also arises in distributed computing applications with download bandwidth constraints, and in human epidemics under quarantine policies. Network epidemics have been extensively studied in literature; prior work characterizes the graph structures required to ensure fast spreading under the SI dynamics, and long lifetime under the SIS dynamics. In particular, these conditions turn out to be meaningful for two classes of networks of practical relevance -- dense, uniform (i.e., clique-like) graphs, and sparse, structured (i.e., star-like) graphs. We show that bounded susceptibility has a surprising impact on epidemic behavior in these graph families. For the SI dynamics, bounded susceptibility has no effect on star-like networks, but dramatically alters the spreading time in clique-like networks. In contrast, for the SIS dynamics, clique-like networks are unaffected, but star-like networks exhibit a sharp change in extinction times under bounded susceptibility. Our findings are useful for the design of disease-resistant networks and infrastructure networks. More generally, they show that results for existing epidemic models are sensitive to modeling assumptions in non-intuitive ways, and suggest caution in directly using these as guidelines for real systems

Differentially Private Trajectory Analysis for Points-of-Interest Recommendation

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet ϵ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees

Scientific Advice to Public Policy-Making

A feature of policy-making today is its dependence on scientific advice to deliver public policies that are robust, credible, and effective. This paper discusses how policy-making profits from scientific advice in areas where science and technology are significant. Particular attention is given to issues holding a high level of uncertainty, either because of inherent variability, because science is incomplete or controversial, or because data are inadequate to support a definitive answer. First, we analyse the social context that characterises the relationship between science and policy-making, with a focus on the decrease of public confidence in politicians and scientists. Second, we compare three different sets of guidelines on the collection and use of expertise in policy-making (issued by the UK, Canada and the European Commission, respectively) and identify two different approaches to scientific advice in policy-making. Third, based on a set of cross-national and multi-disciplinary case studies, we look at how the relationship between science and policy-making works in practice and propose a set of recommendations towards the establishment of a more robust and effective policy-making process.Scientific advice, Policy-making, Expertise