5,709 research outputs found
Small Cuts and Connectivity Certificates: A Fault Tolerant Approach
We revisit classical connectivity problems in the {CONGEST} model of distributed computing. By using techniques from fault tolerant network design, we show improved constructions, some of which are even "local" (i.e., with O~(1) rounds) for problems that are closely related to hard global problems (i.e., with a lower bound of Omega(Diam+sqrt{n}) rounds).
Distributed Minimum Cut: Nanongkai and Su presented a randomized algorithm for computing a (1+epsilon)-approximation of the minimum cut using O~(D +sqrt{n}) rounds where D is the diameter of the graph. For a sufficiently large minimum cut lambda=Omega(sqrt{n}), this is tight due to Das Sarma et al. [FOCS \u2711], Ghaffari and Kuhn [DISC \u2713].
- Small Cuts: A special setting that remains open is where the graph connectivity lambda is small (i.e., constant). The only lower bound for this case is Omega(D), with a matching bound known only for lambda <= 2 due to Pritchard and Thurimella [TALG \u2711]. Recently, Daga, Henzinger, Nanongkai and Saranurak [STOC \u2719] raised the open problem of computing the minimum cut in poly(D) rounds for any lambda=O(1). In this paper, we resolve this problem by presenting a surprisingly simple algorithm, that takes a completely different approach than the existing algorithms. Our algorithm has also the benefit that it computes all minimum cuts in the graph, and naturally extends to vertex cuts as well. At the heart of the algorithm is a graph sampling approach usually used in the context of fault tolerant (FT) design.
- Deterministic Algorithms: While the existing distributed minimum cut algorithms are randomized, our algorithm can be made deterministic within the same round complexity. To obtain this, we introduce a novel definition of universal sets along with their efficient computation. This allows us to derandomize the FT graph sampling technique, which might be of independent interest.
- Computation of all Edge Connectivities: We also consider the more general task of computing the edge connectivity of all the edges in the graph. In the output format, it is required that the endpoints u,v of every edge (u,v) learn the cardinality of the u-v cut in the graph. We provide the first sublinear algorithm for this problem for the case of constant connectivity values. Specifically, by using the recent notion of low-congestion cycle cover, combined with the sampling technique, we compute all edge connectivities in poly(D) * 2^{O(sqrt{log n log log n})} rounds.
Sparse Certificates: For an n-vertex graph G and an integer lambda, a lambda-sparse certificate H is a subgraph H subseteq G with O(lambda n) edges which is lambda-connected iff G is lambda-connected. For D-diameter graphs, constructions of sparse certificates for lambda in {2,3} have been provided by Thurimella [J. Alg. \u2797] and Dori [PODC \u2718] respectively using O~(D) number of rounds. The problem of devising such certificates with o(D+sqrt{n}) rounds was left open by Dori [PODC \u2718] for any lambda >= 4. Using connections to fault tolerant spanners, we considerably improve the round complexity for any lambda in [1,n] and epsilon in (0,1), by showing a construction of (1-epsilon)lambda-sparse certificates with O(lambda n) edges using only O(1/epsilon^2 * log^{2+o(1)} n) rounds
Coarse-Graining and Self-Dissimilarity of Complex Networks
Can complex engineered and biological networks be coarse-grained into smaller
and more understandable versions in which each node represents an entire
pattern in the original network? To address this, we define coarse-graining
units (CGU) as connectivity patterns which can serve as the nodes of a
coarse-grained network, and present algorithms to detect them. We use this
approach to systematically reverse-engineer electronic circuits, forming
understandable high-level maps from incomprehensible transistor wiring: first,
a coarse-grained version in which each node is a gate made of several
transistors is established. Then, the coarse-grained network is itself
coarse-grained, resulting in a high-level blueprint in which each node is a
circuit-module made of multiple gates. We apply our approach also to a
mammalian protein-signaling network, to find a simplified coarse-grained
network with three main signaling channels that correspond to cross-interacting
MAP-kinase cascades. We find that both biological and electronic networks are
'self-dissimilar', with different network motifs found at each level. The
present approach can be used to simplify a wide variety of directed and
nondirected, natural and designed networks.Comment: 11 pages, 11 figure
Extremal Infinite Graph Theory
We survey various aspects of infinite extremal graph theory and prove several
new results. The lead role play the parameters connectivity and degree. This
includes the end degree. Many open problems are suggested.Comment: 41 pages, 16 figure
Local resilience and Hamiltonicity Maker-Breaker games in random-regular graphs
For an increasing monotone graph property \mP the \emph{local resilience}
of a graph with respect to \mP is the minimal for which there exists
of a subgraph with all degrees at most such that the removal
of the edges of from creates a graph that does not possesses \mP.
This notion, which was implicitly studied for some ad-hoc properties, was
recently treated in a more systematic way in a paper by Sudakov and Vu. Most
research conducted with respect to this distance notion focused on the Binomial
random graph model \GNP and some families of pseudo-random graphs with
respect to several graph properties such as containing a perfect matching and
being Hamiltonian, to name a few. In this paper we continue to explore the
local resilience notion, but turn our attention to random and pseudo-random
\emph{regular} graphs of constant degree. We investigate the local resilience
of the typical random -regular graph with respect to edge and vertex
connectivity, containing a perfect matching, and being Hamiltonian. In
particular we prove that for every positive and large enough values
of with high probability the local resilience of the random -regular
graph, \GND, with respect to being Hamiltonian is at least .
We also prove that for the Binomial random graph model \GNP, for every
positive and large enough values of , if
then with high probability the local resilience of \GNP with respect to being
Hamiltonian is at least . Finally, we apply similar
techniques to Positional Games and prove that if is large enough then with
high probability a typical random -regular graph is such that in the
unbiased Maker-Breaker game played on the edges of , Maker has a winning
strategy to create a Hamilton cycle.Comment: 34 pages. 1 figur
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