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authorThibaut Horel <thibaut.horel@gmail.com>2015-02-01 17:49:31 -0500
committerThibaut Horel <thibaut.horel@gmail.com>2015-02-01 17:49:31 -0500
commitc9f0053f279a7899c838aa9640d2643a4f6bbcf8 (patch)
treeed954f7f9a6bd7ffbe16e1d77fc39ca0cbac4ffc
parent97be51465ace29afdea41e9d80fea7b6c344bdda (diff)
downloadcascades-c9f0053f279a7899c838aa9640d2643a4f6bbcf8.tar.gz
Abstract: provide -> prove
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@@ -6,5 +6,5 @@ provided that the number of measurements is $\Omega(s\log m)$ where $s$ is the
maximum degree of the graph and $m$ is the number of nodes.
Furthermore, we show that our algorithm also recovers the edge weights (the
parameters of the diffusion process) and is robust in the context of
-approximate sparsity. Finally we provide an almost matching lower bound of
+approximate sparsity. Finally we prove an almost matching lower bound of
$\Omega(s\log\frac{m}{s})$.