188,227 research outputs found
A graph partition problem
Given a graph on vertices, for which is it possible to partition
the edge set of the -fold complete graph into copies of ? We show
that there is an integer , which we call the \emph{partition modulus of
}, such that the set of values of for which such a partition
exists consists of all but finitely many multiples of . Trivial
divisibility conditions derived from give an integer which divides
; we call the quotient the \emph{partition index of }. It
seems that most graphs have partition index equal to , but we give two
infinite families of graphs for which this is not true. We also compute
for various graphs, and outline some connections between our problem and the
existence of designs of various types
Semidefinite programming and eigenvalue bounds for the graph partition problem
The graph partition problem is the problem of partitioning the vertex set of
a graph into a fixed number of sets of given sizes such that the sum of weights
of edges joining different sets is optimized. In this paper we simplify a known
matrix-lifting semidefinite programming relaxation of the graph partition
problem for several classes of graphs and also show how to aggregate additional
triangle and independent set constraints for graphs with symmetry. We present
an eigenvalue bound for the graph partition problem of a strongly regular
graph, extending a similar result for the equipartition problem. We also derive
a linear programming bound of the graph partition problem for certain Johnson
and Kneser graphs. Using what we call the Laplacian algebra of a graph, we
derive an eigenvalue bound for the graph partition problem that is the first
known closed form bound that is applicable to any graph, thereby extending a
well-known result in spectral graph theory. Finally, we strengthen a known
semidefinite programming relaxation of a specific quadratic assignment problem
and the above-mentioned matrix-lifting semidefinite programming relaxation by
adding two constraints that correspond to assigning two vertices of the graph
to different parts of the partition. This strengthening performs well on highly
symmetric graphs when other relaxations provide weak or trivial bounds
The Complexity of the List Partition Problem for Graphs
The k-partition problem is as follows: Given a graph G and a positive integer k, partition the vertices of G into at most k parts A1, A2, . . . , Ak, where it may be specified that Ai induces a stable set, a clique, or an arbitrary subgraph, and pairs Ai, Aj (i≠j) be completely nonadjacent, completely adjacent, or arbitrarily adjacent. The list k-partition problem generalizes the k-partition problem by specifying for each vertex x, a list L(x) of parts in which it is allowed to be placed. Many well-known graph problems can be formulated as list k-partition problems: e.g., 3-colorability, clique cutset, stable cutset, homogeneous set, skew partition, and 2-clique cutset. We classify, with the exception of two polynomially equivalent problems, each list 4-partition problem as either solvable in polynomial time or NP-complete. In doing so, we provide polynomial-time algorithms for many problems whose polynomial-time solvability was open, including the list 2-clique cutset problem. This also allows us to classify each list generalized 2-clique cutset problem and list generalized skew partition problem as solvable in polynomial time or NP-complete
Streaming Graph Challenge: Stochastic Block Partition
An important objective for analyzing real-world graphs is to achieve scalable
performance on large, streaming graphs. A challenging and relevant example is
the graph partition problem. As a combinatorial problem, graph partition is
NP-hard, but existing relaxation methods provide reasonable approximate
solutions that can be scaled for large graphs. Competitive benchmarks and
challenges have proven to be an effective means to advance state-of-the-art
performance and foster community collaboration. This paper describes a graph
partition challenge with a baseline partition algorithm of sub-quadratic
complexity. The algorithm employs rigorous Bayesian inferential methods based
on a statistical model that captures characteristics of the real-world graphs.
This strong foundation enables the algorithm to address limitations of
well-known graph partition approaches such as modularity maximization. This
paper describes various aspects of the challenge including: (1) the data sets
and streaming graph generator, (2) the baseline partition algorithm with
pseudocode, (3) an argument for the correctness of parallelizing the Bayesian
inference, (4) different parallel computation strategies such as node-based
parallelism and matrix-based parallelism, (5) evaluation metrics for partition
correctness and computational requirements, (6) preliminary timing of a
Python-based demonstration code and the open source C++ code, and (7)
considerations for partitioning the graph in streaming fashion. Data sets and
source code for the algorithm as well as metrics, with detailed documentation
are available at GraphChallenge.org.Comment: To be published in 2017 IEEE High Performance Extreme Computing
Conference (HPEC
A Quasi-Polynomial Time Partition Oracle for Graphs with an Excluded Minor
Motivated by the problem of testing planarity and related properties, we
study the problem of designing efficient {\em partition oracles}. A {\em
partition oracle} is a procedure that, given access to the incidence lists
representation of a bounded-degree graph and a parameter \eps,
when queried on a vertex , returns the part (subset of vertices) which
belongs to in a partition of all graph vertices. The partition should be
such that all parts are small, each part is connected, and if the graph has
certain properties, the total number of edges between parts is at most \eps
|V|. In this work we give a partition oracle for graphs with excluded minors
whose query complexity is quasi-polynomial in 1/\eps, thus improving on the
result of Hassidim et al. ({\em Proceedings of FOCS 2009}) who gave a partition
oracle with query complexity exponential in 1/\eps. This improvement implies
corresponding improvements in the complexity of testing planarity and other
properties that are characterized by excluded minors as well as sublinear-time
approximation algorithms that work under the promise that the graph has an
excluded minor.Comment: 13 pages, 1 figur
Unique Perfect Phylogeny Characterizations via Uniquely Representable Chordal Graphs
The perfect phylogeny problem is a classic problem in computational biology,
where we seek an unrooted phylogeny that is compatible with a set of
qualitative characters. Such a tree exists precisely when an intersection graph
associated with the character set, called the partition intersection graph, can
be triangulated using a restricted set of fill edges. Semple and Steel used the
partition intersection graph to characterize when a character set has a unique
perfect phylogeny. Bordewich, Huber, and Semple showed how to use the partition
intersection graph to find a maximum compatible set of characters. In this
paper, we build on these results, characterizing when a unique perfect
phylogeny exists for a subset of partial characters. Our characterization is
stated in terms of minimal triangulations of the partition intersection graph
that are uniquely representable, also known as ur-chordal graphs. Our
characterization is motivated by the structure of ur-chordal graphs, and the
fact that the block structure of minimal triangulations is mirrored in the
graph that has been triangulated
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