diff --git a/main.tex b/main.tex
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--- a/main.tex
+++ b/main.tex
@@ -587,19 +587,13 @@ Questions:
 
 \newpage
 \section{General graphs proof}
-We consider $R^{(n)}(p)$ as a power series in $p$ and our main aim in this section is to show that $R^{(n)}(p)$ and $R^{(n+k)}(p)$ are the same up to order $n-1$.
 
-The proof will consider variations of the Markov Chain:
-\begin{itemize}
-    \item $\P^{(n)}$ refers to the original process on the length-$n$ cycle.
-    \item $\P^{[a,b]}$ or $\P^{[n]}$ refers to a similar Markov Chain but on a finite chain ($[a,b]$ or $[1,n]$).
-\end{itemize}
-The process on the finite chain has the following modification at the boundary: if a boundary site is resampled, it can only resample itself and its single neighbour so it draws only two new bits. 
+We consider the following generalization of the Markov Chain.
 
-We use the notation $\E^{(n)}$,$\E^{[a,b]}$ and $\E^{[n]}$ similarly for denoting expectations.
+Let $G=(V,E)$ be a graph with vertex set $V$ and edge set $E$. We define a Markov Chain $\mathcal{M}_G$ as the following process: initialize every vertex of $G$ independently to 0 with probability $p$ and 1 with probability $1-p$. Then at each step, select a uniformly random vertex that has value $0$ and resample it and its neighbourhood, all of them independently with the same probability $p$. The Markov Chain terminates when all vertices have value $1$. We use $\P^{G}$ to denote probabilities associated to this Markov Chain.
 
-%Note that an \emph{event} is a subset of all possible paths of the Markov Chain.
-\begin{definition}[Events conditioned on starting state] \label{def:conditionedevents}
+\begin{definition}[Events] \label{def:events}
+    For any subset $S\subseteq V$ of vertices.
     For any state $b\in\{0,1\}^n$, define $\start{b}$ as the event that the starting state of the chain is the state $b$. For any event $A$ and any $v\in[n]$, define
     \begin{align*}
         \P^{(n)}_b(A) &= \P^{(n)}(A \;|\; \start{b}) \\
@@ -608,6 +602,20 @@ We use the notation $\E^{(n)}$,$\E^{[a,b]}$ and $\E^{[n]}$ similarly for denotin
     \end{align*}
     The last two probabilities are not conditioned on any other bits of the starting state.
 \end{definition}
+
+
+$\NZ{S}$
+$\Z{S}$
+
+patch
+
+$B(S;d)$
+
+
+
+We consider $R^{(n)}(p)$ as a power series in $p$ and our main aim in this section is to show that $R^{(n)}(p)$ and $R^{(n+k)}(p)$ are the same up to order $n-1$.
+
+
 %Note that we have $\P^{(n)}(\start{b}) = (1-p)^{|b|}p^{n-|b|}$ by definition of our Markov Chain.
 \begin{definition}[Vertex visiting event] \label{def:visitingResamplings}
     Denote by $\mathrm{Z}^{(v)}$ the event that site $v$ becomes zero at any point in time before the Markov Chain terminates. Denote the complement by $\mathrm{NZ}^{(v)}$, i.e. the event that site $v$ does \emph{not} become zero before it terminates. Furthermore define $\mathrm{NZ}^{(v,w)} := \mathrm{NZ}^{(v)} \cap \mathrm{NZ}^{(w)}$, i.e. the event that \emph{both} $v$ and $w$ do not become zero before termination.