3,617 research outputs found
Graph Isomorphism for unit square graphs
In the past decades for more and more graph classes the Graph Isomorphism
Problem was shown to be solvable in polynomial time. An interesting family of
graph classes arises from intersection graphs of geometric objects. In this
work we show that the Graph Isomorphism Problem for unit square graphs,
intersection graphs of axis-parallel unit squares in the plane, can be solved
in polynomial time. Since the recognition problem for this class of graphs is
NP-hard we can not rely on standard techniques for geometric graphs based on
constructing a canonical realization. Instead, we develop new techniques which
combine structural insights into the class of unit square graphs with
understanding of the automorphism group of such graphs. For the latter we
introduce a generalization of bounded degree graphs which is used to capture
the main structure of unit square graphs. Using group theoretic algorithms we
obtain sufficient information to solve the isomorphism problem for unit square
graphs.Comment: 31 pages, 6 figure
Pure Parsimony Xor Haplotyping
The haplotype resolution from xor-genotype data has been recently formulated
as a new model for genetic studies. The xor-genotype data is a cheaply
obtainable type of data distinguishing heterozygous from homozygous sites
without identifying the homozygous alleles. In this paper we propose a
formulation based on a well-known model used in haplotype inference: pure
parsimony. We exhibit exact solutions of the problem by providing polynomial
time algorithms for some restricted cases and a fixed-parameter algorithm for
the general case. These results are based on some interesting combinatorial
properties of a graph representation of the solutions. Furthermore, we show
that the problem has a polynomial time k-approximation, where k is the maximum
number of xor-genotypes containing a given SNP. Finally, we propose a heuristic
and produce an experimental analysis showing that it scales to real-world large
instances taken from the HapMap project
Community detection and stochastic block models: recent developments
The stochastic block model (SBM) is a random graph model with planted
clusters. It is widely employed as a canonical model to study clustering and
community detection, and provides generally a fertile ground to study the
statistical and computational tradeoffs that arise in network and data
sciences.
This note surveys the recent developments that establish the fundamental
limits for community detection in the SBM, both with respect to
information-theoretic and computational thresholds, and for various recovery
requirements such as exact, partial and weak recovery (a.k.a., detection). The
main results discussed are the phase transitions for exact recovery at the
Chernoff-Hellinger threshold, the phase transition for weak recovery at the
Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial
recovery, the learning of the SBM parameters and the gap between
information-theoretic and computational thresholds.
The note also covers some of the algorithms developed in the quest of
achieving the limits, in particular two-round algorithms via graph-splitting,
semi-definite programming, linearized belief propagation, classical and
nonbacktracking spectral methods. A few open problems are also discussed
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