BEGIN:VCALENDAR
VERSION:2.0
PRODID:ECMLPKDD-MB
BEGIN:VEVENT
DTSTAMP;TZID=Europe/Dublin:20180826T200000
UID:_ecmlpkdd_346
DTSTART;TZID="Europe/Dublin":20180911T110000
DTEND;TZID="Europe/Dublin":20180911T112000
LOCATION:Hogan Mezz 2
TRANSP:TRANSPARENT
SEQUENCE:1
DESCRIPTION: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-type 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 1/3-approximation algorithm, and a 1/5-approximation algorithm with near optimal runtime. For the case of uncertain weighted hypergraphs, we provide a Omega(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.
SUMMARY:Risk-Averse Matchings over Uncertain Graph Databases
CLASS:PUBLIC
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