515 research outputs found
Sketching Cuts in Graphs and Hypergraphs
Sketching and streaming algorithms are in the forefront of current research
directions for cut problems in graphs. In the streaming model, we show that
-approximation for Max-Cut must use space;
moreover, beating -approximation requires polynomial space. For the
sketching model, we show that -uniform hypergraphs admit a
-cut-sparsifier (i.e., a weighted subhypergraph that
approximately preserves all the cuts) with
edges. We also make first steps towards sketching general CSPs (Constraint
Satisfaction Problems)
A Linear-time Algorithm for Sparsification of Unweighted Graphs
Given an undirected graph and an error parameter , the {\em
graph sparsification} problem requires sampling edges in and giving the
sampled edges appropriate weights to obtain a sparse graph with
the following property: the weight of every cut in is within a
factor of of the weight of the corresponding cut in . If
is unweighted, an -time algorithm for constructing
with edges in expectation, and an
-time algorithm for constructing with edges in expectation have recently been developed
(Hariharan-Panigrahi, 2010). In this paper, we improve these results by giving
an -time algorithm for constructing with edges in expectation, for unweighted graphs. Our algorithm is
optimal in terms of its time complexity; further, no efficient algorithm is
known for constructing a sparser . Our algorithm is Monte-Carlo,
i.e. it produces the correct output with high probability, as are all efficient
graph sparsification algorithms
Distributed Minimum Cut Approximation
We study the problem of computing approximate minimum edge cuts by
distributed algorithms. We use a standard synchronous message passing model
where in each round, bits can be transmitted over each edge (a.k.a.
the CONGEST model). We present a distributed algorithm that, for any weighted
graph and any , with high probability finds a cut of size
at most in
rounds, where is the size of the minimum cut. This algorithm is based
on a simple approach for analyzing random edge sampling, which we call the
random layering technique. In addition, we also present another distributed
algorithm, which is based on a centralized algorithm due to Matula [SODA '93],
that with high probability computes a cut of size at most
in rounds for any .
The time complexities of both of these algorithms almost match the
lower bound of Das Sarma et al. [STOC '11], thus
leading to an answer to an open question raised by Elkin [SIGACT-News '04] and
Das Sarma et al. [STOC '11].
Furthermore, we also strengthen the lower bound of Das Sarma et al. by
extending it to unweighted graphs. We show that the same lower bound also holds
for unweighted multigraphs (or equivalently for weighted graphs in which
bits can be transmitted in each round over an edge of weight ),
even if the diameter is . For unweighted simple graphs, we show
that even for networks of diameter , finding an -approximate minimum cut
in networks of edge connectivity or computing an
-approximation of the edge connectivity requires rounds
Graph Sparsification by Edge-Connectivity and Random Spanning Trees
We present new approaches to constructing graph sparsifiers --- weighted
subgraphs for which every cut has the same value as the original graph, up to a
factor of . Our first approach independently samples each
edge with probability inversely proportional to the edge-connectivity
between and . The fact that this approach produces a sparsifier resolves
a question posed by Bencz\'ur and Karger (2002). Concurrent work of Hariharan
and Panigrahi also resolves this question. Our second approach constructs a
sparsifier by forming the union of several uniformly random spanning trees.
Both of our approaches produce sparsifiers with
edges. Our proofs are based on extensions of Karger's contraction algorithm,
which may be of independent interest
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