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Skyline queries are currently the most notable type of multi-criteria search algorithm. A skyline query returns all of the data points in a given a dataset that are not dominated by other data points. However, this type of query is limited by the fact that the number of results cannot be controlled. In some cases, this can result in an excessive number of results, whereas other cases result in an...
Traditional network classification techniques will become computationally intractable when applied on a network which is presented in a streaming fashion with continuous updates. In this paper, we examine the problem of classification in dynamic streaming networks, or graphs. Two scenarios have been considered: the graph transaction scenario and the one large graph scenario. We propose a unified framework...
Currently available artificial network generation models are characterized by consistency and low variance due to the rigidity of models' underlying assumptions. Networks generated from these models are usually too regular and do not contain noise and imbalance inherent in networks induced by human behavior. An important consequence is that much research on social network analysis presented in recent...
Networks evolve at multiple levels; edges or relations may change, and the characteristics of the individuals within the network may change as well. Often these processes are intertwined, and in order to study them, statistical models must be developed that account for coevolution of multiple network components. The increased availability of continuous time network data has prompted new models for...
In this paper we study the temporal evolution of review ratings. We observe that on average ratings tend to become more polarized over time. To explain this phenomenon we propose a simple model that captures the tendency of users for rating manipulation. Simulations with our model demonstrate that it is successful in capturing the aggregate behavior of the users.
The development of social networks has not only improved the online experience, but also stimulated the advances in knowledge mining so as to assist people in planning their offline social events. Users can explore their favorite events, such as celebrations and symposiums, through the pictures and the posts from their friends on social networks. An effective event recommendation can offer great convenience...
We in this paper explore a novel model of influence minimization for the need to effectively prevent the outbreak of epidemic-prone spread on networks. The current network-blocking models usually report the expected number of infected nodes under the limited number of cutting edges. However, to control the epidemic-prone spread such as dengue fever, epidemiologists tend to deploy a cost-effective...
Social influence in online social networks bears resemblance to epidemic spread in networks and has been studied through epidemiological models. The epidemic threshold is a fundamental metric used to evaluate epidemic spread in networks. Previous work has shown that the epidemic threshold of a network is exactly the inverse of the largest eigenvalue of its adjacency matrix. In this work, however,...
The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time fine-grained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts...
In this study, we investigate the problem of network completion by considering the similarities between the node attributes. Given a sample of observed nodes with their incident edges, how can we efficiently reconstruct the network by completing the missing edges of unobserved nodes? Apart from the missing edges, in real settings the node attributes may be partially missing, as well as they may introduce...
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