Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

A theory of tolerance

We develop an economic theory of tolerance where endogenous lifestyles and exogenous traits are invested with symbolic value by people. Value systems are rationally chosen by parents for their children. In conjunction with actual behavior, value systems determine the esteem enjoyed by individuals. Intolerant individuals attach all symbolic value to a small number of attributes and are irrespectful of people with di¤erent ones. Tolerant people have diversi…ed values and respect social alterity. We study the formation of values attached to various types of attributes and identify circumstances under which tolerance spontaneously arises. Policy may a¤ect the evolution of tolerance in distinctive ways, and there may be efficiency as well as equity reasons to promote tolerance. --value systems,tolerance

Hidden in full sight: kinship, science and the law in the aftermath of the Srebrenica genocide

Terms such as “relationship testing,” “familial searching” and “kinship analysis” figure prominently in professional practices of disaster victim identification (DVI). However, despite the dependence of those identification technologies on DNA samples from people who might be related to the dead and despite also the prominence of the notion of “relatedness” as a device for identifying the dead, the concepts of “relatedness” and “kinship” remain elusive both in practice and in analyses of the social and ethical aspects of DVI by DNA; they are hidden in full sight. In this article, we wish to bring kinship more to the fore. We achieve this through a case study of a setting where bio-legal framings dominate, that is, in the trial at the International Criminal Tribunal for the former Yugoslavia (ICTY) of Radovan Karadžić for the Srebrenica genocide in 1995. DNA samples from the families of those massacred in Srebrenica were vital for the identification of individual victims but are now also utilized as “evidence” by both the prosecution and the defense. By viewing practices of science (“evidence” and “identification”) and legal practices (“justice,” “prosecution” and “defence”) through the lens of kinship studies, we will present some alternative and complementary framings for the social accomplishment of ‘relatedness’