From 7d219bcd69ad38af08ca6ba0d0ad025c3fd3cdbe Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Wed, 18 Feb 2015 00:10:48 -0500
Subject: change to suppl

---
 paper/supplementary.tex | 30 ++++++++++++++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

(limited to 'paper/supplementary.tex')

diff --git a/paper/supplementary.tex b/paper/supplementary.tex
index 6a51d95..541c6fb 100644
--- a/paper/supplementary.tex
+++ b/paper/supplementary.tex
@@ -2,6 +2,7 @@
 
 \usepackage{amsmath, amssymb, amsthm}
 \date{}
+\author{}
 
 \title{Supplementary Material \\ {\small Inferring Graphs from Cascades: A Sparse Recovery Framework}}
 
@@ -9,10 +10,35 @@
 
 \maketitle
 
-\subsection*{Approximate Sparsity}
-
 \subsection*{(RE) with high probability}
 
+\subsubsection*{(RE) for the gram matrix}
+
+Recall the expression of the Hessian:
+\begin{equation}
+\nonumber
+    \nabla^2\mathcal{L}(\theta^*)
+    = \frac{1}{|\mathcal{T}|}\sum_{t\in\mathcal{T}}x^t(x^t)^T
+    \bigg[x_i^{t+1}\frac{f''f-f'^2}{f^2}(\theta^* \cdot x^t)\\
+    -(1-x_i^{t+1})\frac{f''(1-f) + f'^2}{(1-f)^2}(\theta^* \cdot x^t)\bigg]
+\end{equation}
+
+This can be rewritten as $X J X^T$ where $J$ is a diagonal matrix with coefficients either $g_1$ or $g_2$. Under certain regularity conditions for $f$ (such as strong log-concavity), we can consider only the gram matrix: $\Delta^T X^T J X \Delta \geq \gamma_1 \Delta^T X^T X \Delta$
+
+Denote $p_i$ the probability that node $i$ is infected at the beginning of the cascade. Let $\mathbb{P}(i)$ be the probability that $i$ is infected at one point during a cascade. Let $\mathbb{P}(S)$ for any set $S \subseteq N$ be the probability that $S$ is infected (all at once) at one point during a cascade. The diagonal of the Gram Matrix can be written as:
+\begin{equation}
+\nonumber
+c_j := \mathbb{P}(i) = p_i - \sum_{S \subseteq {\cal N}(i)} (-1)^{|S|} \mathbb{P}(S) \prod_{j \in S} w_{ji}  =  p_i + \sum_{j \in N(i)} w_{ji}c_j + \dots
+\end{equation}
+Note that we have the following bounds for $c_j$:
+
+\begin{align}
+c_j &\leq p_i + \sum_{j \in S} w_{ji} c_j \tag{PageRank}\\
+c_j &\geq p_i + \max_{j \in S} w_{ji} c_j \tag{?}
+\end{align}
+
+\subsection*{Relaxed (RE) with high probability}
+
 \subsection*{Lower Bound}
 
 \end{document}
\ No newline at end of file
-- 
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