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authorThibaut Horel <thibaut.horel@gmail.com>2015-09-21 16:15:13 -0400
committerThibaut Horel <thibaut.horel@gmail.com>2015-09-21 16:15:13 -0400
commit8c2d3d070a3db1b469e3e32e3c20ef67bc274b3b (patch)
tree2f65df75e18be3deaa497ba66237a27ea01e0b56 /supplements
parent2fd2ce34df46fca46b365d983a650069911e5558 (diff)
downloadcriminal_cascades-8c2d3d070a3db1b469e3e32e3c20ef67bc274b3b.tar.gz
Supplements, section 3.1
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@@ -295,9 +295,34 @@ the optimum:
\section{Inferring the Patterns of Infections}
-We can estimate if a person was primarily infected via peer contagion by comparing the contributions from the background rate and from his or her peers.
+\subsection{Methodology}
-We take this approach one step further to determine the person most responsible for infecting each of these 7,016 individuals infected by social contagion.
+Given fitted values of the parameters of the Hawkes contagion model, it is then
+possible to determine whether an infection event $(t, v)$ was primarily caused
+by the exogenous process or endogenous peer contagion. For this, using formula
+\eqref{eq:hawkes}, we simply compare the value of the exogenous intensity and
+the sum of endogenous exciting functions at the time $t$ of infection, and
+attribute the infection event to the largest of the two quantities. In other
+words, for our contagion model we compare:
+\begin{enumerate}
+ \item $\mu(t) = \mu(t) = \mu_0\left[1 + 0.43 \sin\left(\frac{2\pi}{365.24}
+ t + 4.36\right)\right]$ and
+\item $\sum_{i:t_i< t} g_\alpha(u_i, v)\betae^{-\beta(t-t_i)}$
+\end{enumerate}
+
+It is possible to go one step further: for an infection event attributed to
+peer contagion, we can single out a single past infection event as its cause.
+This is achieved by choosing the event $i$ for which the value of the summand
+in 2. is maximum. That is, the event $i$ for which the value of $g_\alpha(u_i, v)\beta
+e^{-\beta(t-t_i)}$ is largest.
+
+We thus uncover the patterns of infections: each infection event is attributed
+to either the exogenous intensity or a single past infection event. Drawing an
+edge between infection events $(t_1, u_1)$ and $(t_2, u_2)$ if $(t_1, u_1)$ is
+the cause of $(t_2, u_2)$, we obtain the forest of infections: tree roots are
+the infection events attributed to exogenous intensity, and tree nodes are
+events attributed to peer contagion and are connected to their cause. We note
+that cycles are impossible since edges are directed forward in time.
\begin{figure}
\centering
@@ -319,6 +344,6 @@ We also simulated contagions on the co-offending network. Since we are most inte
\subsubsection{Results}
-\section{Regarding causality [THIBAUT WRITE THIS SECTION]}
+\subsection{Comments on Causality}
\end{document}