From 39a72f9f8127970470f46529ff8f1cc1a22450d9 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Mon, 18 May 2015 23:32:07 +0200
Subject: More changes in the appendix

---
 paper/sections/appendix.tex | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'paper/sections/appendix.tex')

diff --git a/paper/sections/appendix.tex b/paper/sections/appendix.tex
index 5a6a2c7..32e2775 100644
--- a/paper/sections/appendix.tex
+++ b/paper/sections/appendix.tex
@@ -134,7 +134,7 @@ Figure~\ref{fig:running_time_n_nodes}.}
 
 We include here a running time analysis of our algorithm. In
 Figure~\ref{fig:running_time_n_nodes}, we compared our algorithm to the
-benchmarks for increasing values of the number of nodes. In
+benchmark algorithms for increasing values of the number of nodes. In
 Figure~\ref{fig:running_time_n_cascades}, we compared our algorithm to the
 benchmarks for a fixed graph but for increasing number of observed cascades.
 
@@ -143,9 +143,9 @@ Even though both the MLE algorithm and the algorithm we introduced are based on
 convex optimization, the MLE algorithm is faster. This is due to the overhead
 caused by the $\ell_1$-regularisation in~\eqref{eq:pre-mle}.
 
-The dependency as the number of cascades increases is linear, as expected. The
-slope is largest for our algorithm, which is against caused by the overhead
-induced by the $\ell_1$-regularization.
+The dependency of the running time on the number of cascades increases is
+linear, as expected. The slope is largest for our algorithm, which is against
+caused by the overhead induced by the $\ell_1$-regularization.
 
 
 
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