+\lemma\id{contiter}%
+Each iteration of the algorithm can be carried out in time~$\O(m)$.
+
+\proof
+The only non-trivial parts are steps 6 and~7. Contractions can be handled similarly
+to the unions in the original Bor\o{u}vka's algorithm (see \ref{boruvkaiter}):
+We build an auxillary graph containing only the selected edges~$e_i$, find
+connected components of this graph and renumber vertices in each component to
+the identifier of the component. This takes $\O(m)$ time.
+
+Flattening is performed by first removing the loops and then bucket-sorting the edges
+(as ordered pairs of vertex identifiers) lexicographically, which brings parallel
+edges together. The bucket sort uses two passes with $n$~buckets, so it takes
+$\O(n+m)=\O(m)$.
+\qed
+
+\thm
+The Contractive Bor\o{u}vka's algorithm finds the MST in time $\O(m\log n)$.
+
+\proof
+As in the original Bor\o{u}vka's algorithm, the number of iterations is $\O(\log n)$.
+Then apply the previous lemma.
+\qed
+
+\thmn{\cite{mm:mst}}\id{planarbor}%
+When the input graph is planar, the Contractive Bor\o{u}vka's algorithm runs in
+time $\O(n)$.
+
+\proof
+Let us denote the graph considered by the algorithm at the beginning of the $i$-th
+iteration by $G_i$ (starting with $G_0=G$) and its number of vertices and edges
+by $n_i$ and $m_i$ respectively. As we already know from the previous lemma,
+the $i$-th iteration takes $\O(m_i)$ time. We are going to prove that the
+$m_i$'s are decreasing geometrically.
+
+The number of trees in the non-contracting version of the algorithm drops
+at least by a factor of two in each iteration (Lemma \ref{boruvkadrop}) and the
+same must hold for the number of vertices in the contracting version.
+Therefore $n_i\le n/2^i$.
+
+However, every $G_i$ is planar, because the class of planar graphs is closed
+under edge deletion and contraction. The~$G_i$ is also simple as we explicitly removed multiple edges and
+loops at the end of the previous iteration. Hence we can use the standard theorem on
+the number of edges of planar simple graphs (see for example \cite{diestel:gt}) to get $m_i\le 3n_i \le 3n/2^i$.
+From this we get that the total time complexity is $\O(\sum_i m_i)=\O(\sum_i n/2^i)=\O(n)$.
+\qed
+
+\rem
+There are several other possibilities how to find the MST of a planar graph in linear time.
+For example, Matsui \cite{matsui:planar} has described an algorithm based on simultaneously
+working on the graph and its topological dual. The advantage of our approach is that we do not need
+to construct the planar embedding explicitly. We will show one more linear algorithm
+in section~\ref{minorclosed}.
+
+\rem
+To achieve the linear time complexity, the algorithm needs a very careful implementation,
+but we defer the technical details to section~\ref{bucketsort}.
+
+\para
+Graph contractions are indeed a~very powerful tool and they can be used in other MST
+algorithms as well. The following lemma shows the gist:
+
+\lemman{Contraction of MST edges}\id{contlemma}%
+Let $G$ be a weighted graph, $e$~an arbitrary edge of~$\mst(G)$, $G/e$ the multigraph
+produced by contracting $G$ along~$e$, and $\pi$ the bijection between edges of~$G-e$ and
+their counterparts in~$G/e$. Then: $$\mst(G) = \pi^{-1}[\mst(G/e)] + e.$$
+
+\proof
+% We seem not to need this lemma for multigraphs...
+%If there are any loops or parallel edges in~$G$, we can flatten the graph. According to the
+%Flattening lemma (\ref{flattening}), the MST stays the same and if we remove a parallel edge
+%or loop~$f$, then $\pi(f)$ would be removed when flattening~$G/e$, so $f$ never participates
+%in a MST.
+The right-hand side of the equality is a spanning tree of~$G$, let us denote it by~$T$ and
+the MST of $G/e$ by~$T'$. If $T$ were not minimum, there would exist a $T$-light edge~$f$ in~$G$
+(by Theorem \ref{mstthm}). If the path $T[f]$ covered by~$f$ does not contain~$e$,
+then $\pi[T[f]]$ is a path covered by~$\pi(f)$ in~$T'$. Otherwise $\pi(T[f]-e)$ is such a path.
+In both cases, $f$ is $T'$-light, which contradicts the minimality of~$T'$. (We do not have
+a~multigraph version of the theorem, but the side we need is a~straightforward edge exchange,
+which obviously works in multigraphs as well.)
+\qed
+
+\rem
+In the previous algorithm, the role of the mapping~$\pi^{-1}$ is of course played by the edge labels~$\ell$.
+
+Finally, we will show a family of graphs where the $\O(m\log n)$ bound on time complexity
+is tight. The graphs do not have unique weights, but they are constructed in a way that
+the algorithm never compares two edges with the same weight. Therefore, when two such
+graphs are monotonely isomorphic (see~\ref{mstiso}), the algorithm processes them in the same way.
+
+\defn
+A~\df{distractor of order~$k$,} denoted by~$D_k$, is a path on $n=2^k$~vertices $v_1,\ldots,v_n$
+where each edge $v_iv_{i+1}$ has its weight equal to the number of trailing zeroes in the binary
+representation of the number~$i$. The vertex $v_1$ is called a~\df{base} of the distractor.
+
+\rem
+Alternatively, we can use a recursive definition: $D_0$ is a single vertex, $D_{k+1}$ consists
+of two disjoint copies of~$D_k$ joined by an edge of weight~$k$.
+
+\figure{distractor.eps}{\epsfxsize}{A~distractor $D_3$ and its evolution (bold edges are contracted)}
+
+\lemma
+A~single iteration of the contractive algorithm reduces~$D_k$ to a graph isomorphic with~$D_{k-1}$.
+
+\proof
+Each vertex~$v$ of~$D_k$ is incident with a single edge of weight~1. The algorithm therefore
+selects all weight~1 edges and contracts them. This produces a graph which is
+exactly $D_{k-1}$ with all weights increased by~1, which does not change the relative order of edges.
+\qed
+
+\defn
+A~\df{hedgehog}~$H_{a,k}$ is a graph consisting of $a$~distractors $D_k^1,\ldots,D_k^a$ of order~$k$
+together with edges of a complete graph on the bases of the distractors. These additional edges
+have arbitrary weights, but heavier than the edges of all distractors.
+
+\figure{hedgehog.eps}{\epsfxsize}{A~hedgehog $H_{5,2}$ (quills bent to fit in the picture)}
+
+\lemma
+A~single iteration of the contractive algorithm reduces~$H_{a,k}$ to a graph isomorphic with $H_{a,k-1}$.
+
+\proof
+Each vertex is incident with an edge of some distractor, so the algorithm does not select
+any edge of the complete graph. Contraction therefore reduces each distractor to a smaller
+distractor (modulo an additive factor in weight) and leaves the complete graph intact.
+This is monotonely isomorphic to $H_{a,k-1}$.
+\qed
+
+\thmn{Lower bound for Contractive Bor\o{u}vka}%
+For each $n$ there exists a graph on $\Theta(n)$ vertices and $\Theta(n)$ edges
+such that the Contractive Bor\o{u}vka's algorithm spends time $\Omega(n\log n)$ on it.
+
+\proof
+Consider the hedgehog $H_{a,k}$ for $a=\lceil\sqrt n\rceil$ and $k=\lceil\log_2 a\rceil$.
+It has $a\cdot 2^k = \Theta(n)$ vertices and ${a \choose 2} + a\cdot 2^k = \Theta(a^2) + \Theta(a^2) = \Theta(n)$ edges
+as we wanted.
+
+By the previous lemma, the algorithm proceeds through a sequence of hedgehogs $H_{a,k},
+H_{a,k-1}, \ldots, H_{a,0}$ (up to monotone isomorphism), so it needs a logarithmic number of iterations plus some more
+to finish on the remaining complete graph. Each iteration runs on a graph with $\Omega(n)$
+edges as every $H_{a,k}$ contains a complete graph on~$a$ vertices.
+\qed