From 0ff14f56819acfc7be77f9237e18417d465b2266 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Fri, 6 Feb 2015 15:48:24 -0500
Subject: Compression

---
 paper/sections/model.tex | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

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

diff --git a/paper/sections/model.tex b/paper/sections/model.tex
index 36829f2..d8706bc 100644
--- a/paper/sections/model.tex
+++ b/paper/sections/model.tex
@@ -12,8 +12,7 @@ state space $\{0, 1, \dots, K-1\}^V$ with the following properties:
     that all transition probabilities of the Markov process are either constant
     or can be expressed as a function of the graph parameters $\Theta$ and the
     set of ``contagious nodes'' at the previous time step.
-\item The initial probability over $\{0, 1, \dots, K-1\}^V$ of the Cascade
-    Model is such that all nodes can eventually reach a \emph{contagious state}
+\item The initial probability over $\{0, 1, \dots, K-1\}^V$ is such that all nodes can eventually reach a \emph{contagious state}
     with non-zero probability. The ``contagious'' nodes at $t=0$ are called
     \emph{source nodes}.
 \end{enumerate}
@@ -26,7 +25,7 @@ the ``single source'' assumption made in \cite{Daneshmand:2014} and
 \cite{Abrahao:13} as well as the ``uniformly chosen source set'' assumption
 made in \cite{Netrapalli:2012} verify condition 3.
 
-In the context of Graph Inference, \cite{Netrapalli:2012} focuses specifically
+In the context of Graph Inference, \cite{Netrapalli:2012} focus
 on the well-known discrete-time independent cascade model recalled below, which
 \cite{Abrahao:13} and \cite{Daneshmand:2014} generalize to continuous time. We
 extend the independent cascade model in a different direction by considering
-- 
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