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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2015-02-01 17:49:31 -0500 |
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| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2015-02-01 17:49:31 -0500 |
| commit | c9f0053f279a7899c838aa9640d2643a4f6bbcf8 (patch) | |
| tree | ed954f7f9a6bd7ffbe16e1d77fc39ca0cbac4ffc | |
| parent | 97be51465ace29afdea41e9d80fea7b6c344bdda (diff) | |
| download | cascades-c9f0053f279a7899c838aa9640d2643a4f6bbcf8.tar.gz | |
Abstract: provide -> prove
| -rw-r--r-- | paper/sections/abstract.tex | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/paper/sections/abstract.tex b/paper/sections/abstract.tex index 9136be7..ad5b893 100644 --- a/paper/sections/abstract.tex +++ b/paper/sections/abstract.tex @@ -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 $\Omega(s\log\frac{m}{s})$. |
