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| author | ericbalkanski <ericbalkanski@MACD-01953.local> | 2014-12-07 15:16:18 -0500 |
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| committer | ericbalkanski <ericbalkanski@MACD-01953.local> | 2014-12-07 15:16:18 -0500 |
| commit | aca5c88f9856c9cdd676e22e44e1955c3e75f676 (patch) | |
| tree | dbb7b3316c905cfa07afd5fba7e5a74fd34affa7 /notes/reportYaron.tex | |
| parent | 830e7fdc86c10d22bca2694f2a1da276cd1c8f60 (diff) | |
| download | cascades-aca5c88f9856c9cdd676e22e44e1955c3e75f676.tar.gz | |
Added examples to report
Diffstat (limited to 'notes/reportYaron.tex')
| -rw-r--r-- | notes/reportYaron.tex | 73 |
1 files changed, 73 insertions, 0 deletions
diff --git a/notes/reportYaron.tex b/notes/reportYaron.tex index d39d060..1dc0db1 100644 --- a/notes/reportYaron.tex +++ b/notes/reportYaron.tex @@ -84,6 +84,42 @@ If the vector $\vec x_i$ is sufficiently sparse, i.e. node $i$ has sufficiently \subsection{Example} + +\begin{figure} +\centering +\includegraphics[width=0.3\textwidth]{voter.png} +\caption{A cascade in the voter model with time steps $t = 0,1,2$ over a graph with 5 vertices} +\end{figure} + + + + +To recover the neighbors of $v_1$, we get the following matrix $M$ for the example in Figure 1: + +\begin{align*} +&\hspace{0.35cm} v_2 \hspace{0.2 cm} v_3 \hspace{0.2 cm} v_4 \hspace{0.2 cm} v_5 &\\ +\vspace{1 cm} +M = & \left( \begin{array}{cccc} +0 & 0 & 1 & 1 \\ +1 & 1 & 0 & 0 \\ +\end{array} \right) & \begin{array}{l} \hspace{ -4.5 cm} +\text{time step 0} \\ + \hspace{ - 4.5cm} \text{time step 1} \\ +\end{array} +\end{align*} + +and the vector $b_1$: + +\begin{align*} +b_1 = & \left( \begin{array}{c} +1 \\ +1 \\ +\end{array} \right) & \begin{array}{l} \hspace{ - 5cm} +\text{time step 1} \\ + \hspace{ - 5cm} \text{time step 2} \\ + \end{array} +\end{align*} + \section{The Independent Cascade Model} \subsection{Description} @@ -127,6 +163,43 @@ e^{M_i \vec \theta_i} = 1 - \vec b_i + \vec \epsilon_i Note that this is not exactly a sparse recovery model due to the non-linear exponential term. It is of the author's opinion however that if the probabilities $p_{j,i}$ are restrained to a bounded interval $[0, 1- \eta]$, then most of the results which hold for the linear voter model will continue to hold in this case. +\subsection{Example} + +\begin{figure} +\centering +\includegraphics[width=0.3\textwidth]{icc.png} +\caption{A cascade in the independent cascade model with time steps $t = 0,1,2$ over a graph with 5 vertices} +\end{figure} + + + +To recover the neighbors of $v_5$, we get the following matrix $M$ for the example in Figure 2: +\begin{align*} +&v_1 \hspace{0.2 cm} v_2 \hspace{0.2 cm} v_3 \hspace{0.2 cm} v_4 \\ +\vspace{1 cm} +M = & \left( \begin{array}{cccc} +1 & 0 & 0 & 0 \\ +0 & 1 & 1 & 0 \\ +\end{array} \right) \begin{array}{l} \hspace{ 1cm} +\text{time step 0} \\ + \hspace{ 1cm} \text{time step 1} \\ + \end{array} +\end{align*} + +and the vector $b_5$: + +\begin{align*} +b_5 = & \left( \begin{array}{c} +0 \\ +1 \\ +\end{array} \right) \begin{array}{l} \hspace{ 1cm} +\text{time step 1} \\ + \hspace{ 1cm} \text{time step 2} \\ + \end{array} +\end{align*} + + + \section{Sparse Recovery} \subsection{Introduction} |
