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Two sample tests in structured networks
Finanzierung:
Deutsche Forschungsgemeinschaft (DFG) ;
The study of networks leads to a wide range of high dimensional inference problems. In most practical scenarios, one needs to draw inference from a small population of large networks. The present paper studies hypothesis testing of graphs in this high-dimensional regime. We consider the problem of testing between two populations of inhomogeneous random graphs defined on the same set of vertices. We are interested in tests based on estimates of the Frobenius and operator norms of the difference between the population adjacency matrices, in both the "large graph, small sample" and "small graph, large sample" regimes. We aim at deriving lower bounds on the minimax separation rate for the associated testing problems, and show that the constructed tests are near optimal. We plan to focus on the structured graph setting (e.g. sparse graphs, etc).

Schlagworte

structured networks
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