From 09e64e8bc5ff34ce9886e56cd918c2e03c45b283 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Mon, 18 May 2015 14:13:22 +0200
Subject: Pass on introduction.

Add comment on correlated measurements
---
 paper/sections/intro.tex | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

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

diff --git a/paper/sections/intro.tex b/paper/sections/intro.tex
index 7688aeb..4c18faf 100644
--- a/paper/sections/intro.tex
+++ b/paper/sections/intro.tex
@@ -65,11 +65,12 @@ A more recent line of research~\cite{Daneshmand:2014} has focused on applying
 advances in sparse recovery to the graph inference problem. Indeed, the graph
 can be interpreted as a ``sparse signal'' measured through influence cascades
 and then recovered.  The challenge is that influence cascade models typically
-lead to non-linear inverse problems. The sparse recovery literature suggests
-that $\Omega(s\log\frac{m}{s})$ cascade observations should be sufficient to
-recover the graph~\cite{donoho2006compressed, candes2006near}. However, the
-best known upper bound to this day is $\O(s^2\log m)$~\cite{Netrapalli:2012,
-Daneshmand:2014}
+lead to non-linear inverse problems and the measurements (the state of the
+nodes at different time steps) are usually correlated. The sparse recovery
+literature suggests that $\Omega(s\log\frac{m}{s})$ cascade observations should
+be sufficient to recover the graph~\cite{donoho2006compressed, candes2006near}.
+However, the best known upper bound to this day is $\O(s^2\log
+m)$~\cite{Netrapalli:2012, Daneshmand:2014}
 
 
 The contributions of this paper are the following:
-- 
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