84 research outputs found
A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities
Distributed minimum spanning tree (MST) problem is one of the most central
and fundamental problems in distributed graph algorithms. Garay et al.
\cite{GKP98,KP98} devised an algorithm with running time , where is the hop-diameter of the input -vertex -edge
graph, and with message complexity . Peleg and Rubinovich
\cite{PR99} showed that the running time of the algorithm of \cite{KP98} is
essentially tight, and asked if one can achieve near-optimal running time
**together with near-optimal message complexity**.
In a recent breakthrough, Pandurangan et al. \cite{PRS16} answered this
question in the affirmative, and devised a **randomized** algorithm with time
and message complexity . They asked if
such a simultaneous time- and message-optimality can be achieved by a
**deterministic** algorithm.
In this paper, building upon the work of \cite{PRS16}, we answer this
question in the affirmative, and devise a **deterministic** algorithm that
computes MST in time , using messages. The polylogarithmic factors in the time
and message complexities of our algorithm are significantly smaller than the
respective factors in the result of \cite{PRS16}. Also, our algorithm and its
analysis are very **simple** and self-contained, as opposed to rather
complicated previous sublinear-time algorithms \cite{GKP98,KP98,E04b,PRS16}
Almost-Tight Distributed Minimum Cut Algorithms
We study the problem of computing the minimum cut in a weighted distributed
message-passing networks (the CONGEST model). Let be the minimum cut,
be the number of nodes in the network, and be the network diameter. Our
algorithm can compute exactly in time. To the best of our knowledge, this is the first paper that
explicitly studies computing the exact minimum cut in the distributed setting.
Previously, non-trivial sublinear time algorithms for this problem are known
only for unweighted graphs when due to Pritchard and
Thurimella's -time and -time algorithms for
computing -edge-connected and -edge-connected components.
By using the edge sampling technique of Karger's, we can convert this
algorithm into a -approximation -time algorithm for any . This improves
over the previous -approximation -time algorithm and
-approximation -time algorithm of Ghaffari and Kuhn. Due to the lower
bound of by Das Sarma et al. which holds for any
approximation algorithm, this running time is tight up to a factor.
To get the stated running time, we developed an approximation algorithm which
combines the ideas of Thorup's algorithm and Matula's contraction algorithm. It
saves an factor as compared to applying Thorup's tree
packing theorem directly. Then, we combine Kutten and Peleg's tree partitioning
algorithm and Karger's dynamic programming to achieve an efficient distributed
algorithm that finds the minimum cut when we are given a spanning tree that
crosses the minimum cut exactly once
Distributed Approximation of Minimum Routing Cost Trees
We study the NP-hard problem of approximating a Minimum Routing Cost Spanning
Tree in the message passing model with limited bandwidth (CONGEST model). In
this problem one tries to find a spanning tree of a graph over nodes
that minimizes the sum of distances between all pairs of nodes. In the
considered model every node can transmit a different (but short) message to
each of its neighbors in each synchronous round. We provide a randomized
-approximation with runtime for
unweighted graphs. Here, is the diameter of . This improves over both,
the (expected) approximation factor and the runtime
of the best previously known algorithm.
Due to stating our results in a very general way, we also derive an (optimal)
runtime of when considering -approximations as done by the
best previously known algorithm. In addition we derive a deterministic
-approximation
Distributed Testing of Excluded Subgraphs
We study property testing in the context of distributed computing, under the
classical CONGEST model. It is known that testing whether a graph is
triangle-free can be done in a constant number of rounds, where the constant
depends on how far the input graph is from being triangle-free. We show that,
for every connected 4-node graph H, testing whether a graph is H-free can be
done in a constant number of rounds too. The constant also depends on how far
the input graph is from being H-free, and the dependence is identical to the
one in the case of testing triangles. Hence, in particular, testing whether a
graph is K_4-free, and testing whether a graph is C_4-free can be done in a
constant number of rounds (where K_k denotes the k-node clique, and C_k denotes
the k-node cycle). On the other hand, we show that testing K_k-freeness and
C_k-freeness for k>4 appear to be much harder. Specifically, we investigate two
natural types of generic algorithms for testing H-freeness, called DFS tester
and BFS tester. The latter captures the previously known algorithm to test the
presence of triangles, while the former captures our generic algorithm to test
the presence of a 4-node graph pattern H. We prove that both DFS and BFS
testers fail to test K_k-freeness and C_k-freeness in a constant number of
rounds for k>4
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