From 27b55f70aeb9025560481a1756eca03b8eabd0a1 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Thu, 21 May 2015 12:07:53 +0200
Subject: Fix the paper for arxiv submission

---
 paper/sections/experiments.tex | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

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

diff --git a/paper/sections/experiments.tex b/paper/sections/experiments.tex
index 7336f93..58077a7 100644
--- a/paper/sections/experiments.tex
+++ b/paper/sections/experiments.tex
@@ -9,7 +9,7 @@
 %graphs, nbr of measurements vs.~number of cascades. One common metric for all
 %types of graphs (possibly the least impressive improvement)}
 
-\begin{table*}[t]
+\begin{figure*}[t]
 \centering
 \begin{tabular}{l l l}
     \hspace{-0.5em}\includegraphics[scale=.28]{figures/barabasi_albert.pdf}
@@ -27,7 +27,7 @@
  ($\ell_2$-norm \emph{vs.} $n$) & (f) Watts-Strogatz (F$1$ \emph{vs.}
  $p_{\text{init}}$)
 \end{tabular}
-\captionof{figure}{Figures (a) and (b) report the F$1$-score in $\log$ scale for
+\caption{Figures (a) and (b) report the F$1$-score in $\log$ scale for
   2 graphs as a function of the number of cascades $n$: (a) Barabasi-Albert
   graph, $300$ nodes, $16200$ edges. (b) Watts-Strogatz graph, $300$ nodes,
   $4500$ edges. Figure (c) plots the Precision-Recall curve for various values
@@ -37,7 +37,7 @@ graph which is: (d) exactly sparse (e) non-exactly sparse, as a function of the
 number of cascades $n$. Figure (f) plots the F$1$-score for the Watts-Strogatz
 graph as a function of $p_{init}$.}~\label{fig:four_figs}
 \vspace{-2em}
-\end{table*}
+\end{figure*}
 
 In this section, we validate empirically the results and assumptions of
 Section~\ref{sec:results} for varying levels of sparsity and different
-- 
cgit v1.2.3-70-g09d2

