879,195 research outputs found
Clustering and Community Detection with Imbalanced Clusters
Spectral clustering methods which are frequently used in clustering and
community detection applications are sensitive to the specific graph
constructions particularly when imbalanced clusters are present. We show that
ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to
imbalanced cluster sizes since they tend to emphasize cut sizes over cut
values. We propose a graph partitioning problem that seeks minimum cut
partitions under minimum size constraints on partitions to deal with imbalanced
cluster sizes. Our approach parameterizes a family of graphs by adaptively
modulating node degrees on a fixed node set, yielding a set of parameter
dependent cuts reflecting varying levels of imbalance. The solution to our
problem is then obtained by optimizing over these parameters. We present
rigorous limit cut analysis results to justify our approach and demonstrate the
superiority of our method through experiments on synthetic and real datasets
for data clustering, semi-supervised learning and community detection.Comment: Extended version of arXiv:1309.2303 with new applications. Accepted
to IEEE TSIP
On bounding the bandwidth of graphs with symmetry
We derive a new lower bound for the bandwidth of a graph that is based on a
new lower bound for the minimum cut problem. Our new semidefinite programming
relaxation of the minimum cut problem is obtained by strengthening the known
semidefinite programming relaxation for the quadratic assignment problem (or
for the graph partition problem) by fixing two vertices in the graph; one on
each side of the cut. This fixing results in several smaller subproblems that
need to be solved to obtain the new bound. In order to efficiently solve these
subproblems we exploit symmetry in the data; that is, both symmetry in the
min-cut problem and symmetry in the graphs. To obtain upper bounds for the
bandwidth of graphs with symmetry, we develop a heuristic approach based on the
well-known reverse Cuthill-McKee algorithm, and that improves significantly its
performance on the tested graphs. Our approaches result in the best known lower
and upper bounds for the bandwidth of all graphs under consideration, i.e.,
Hamming graphs, 3-dimensional generalized Hamming graphs, Johnson graphs, and
Kneser graphs, with up to 216 vertices
Network Flow Optimization for Restoration of Images
The network flow optimization approach is offered for restoration of
grayscale and color images corrupted by noise. The Ising models are used as a
statistical background of the proposed method. The new multiresolution network
flow minimum cut algorithm, which is especially efficient in identification of
the maximum a posteriori estimates of corrupted images, is presented. The
algorithm is able to compute the MAP estimates of large size images and can be
used in a concurrent mode. We also describe the efficient solutions of the
problem of integer minimization of two energy functions for the Ising models of
gray-scale and color images
Weighted Min-Cut: Sequential, Cut-Query and Streaming Algorithms
Consider the following 2-respecting min-cut problem. Given a weighted graph
and its spanning tree , find the minimum cut among the cuts that contain
at most two edges in . This problem is an important subroutine in Karger's
celebrated randomized near-linear-time min-cut algorithm [STOC'96]. We present
a new approach for this problem which can be easily implemented in many
settings, leading to the following randomized min-cut algorithms for weighted
graphs.
* An -time sequential algorithm:
This improves Karger's and bounds when the input graph is not extremely
sparse or dense. Improvements over Karger's bounds were previously known only
under a rather strong assumption that the input graph is simple [Henzinger et
al. SODA'17; Ghaffari et al. SODA'20]. For unweighted graphs with parallel
edges, our bound can be improved to .
* An algorithm requiring cut queries to compute the min-cut of
a weighted graph: This answers an open problem by Rubinstein et al. ITCS'18,
who obtained a similar bound for simple graphs.
* A streaming algorithm that requires space and
passes to compute the min-cut: The only previous non-trivial exact min-cut
algorithm in this setting is the 2-pass -space algorithm on simple
graphs [Rubinstein et al., ITCS'18] (observed by Assadi et al. STOC'19).
In contrast to Karger's 2-respecting min-cut algorithm which deploys
sophisticated dynamic programming techniques, our approach exploits some cute
structural properties so that it only needs to compute the values of cuts corresponding to removing pairs of tree edges, an
operation that can be done quickly in many settings.Comment: Updates on this version: (1) Minor corrections in Section 5.1, 5.2;
(2) Reference to newer results by GMW SOSA21 (arXiv:2008.02060v2), DEMN
STOC21 (arXiv:2004.09129v2) and LMN 21 (arXiv:2102.06565v1
Solving an integrated job-shop problem with human resource constraints
International audienceWe propose two exact methods to solve an integrated employee-timetable and job-shop-scheduling problem. The problem is to find a minimum cost employee-timetable, where employees have different competences and work during shifts, so that the production, that corresponds to a job-shop with resource availability constraints, can be achieved. We introduce two new exact procedures: (1) a decomposition and cut generation approach and (2) a hybridization of a cut generation process with a branch and bound strategy. We also propose initial cuts that strongly improve these methods as well as a standard MIP approach. The computational performances of those methods on benchmark instances are compared to that of other methods from the literature
OrderedCuts: A new approach for computing Gomory-Hu tree
The Gomory-Hu tree, or a cut tree, is a classic data structure that stores
minimum - cuts of an undirected weighted graph for all pairs of nodes
. We propose a new approach for computing the cut tree based on a
reduction to the problem that we call OrderedCuts. Given a sequence of nodes
, its goal is to compute minimum
- cuts for all . We show that the
cut tree can be computed by calls to OrderedCuts. We also
establish new results for OrderedCuts that may be of independent interest.
First, we prove that all cuts can be stored compactly with space
in a data structure that we call an OC tree. We define a weaker version of this
structure that we call depth-1 OC tree, and show that it is sufficient for
constructing the cut tree. Second, we prove results that allow
divide-and-conquer algorithms for computing OC tree. We argue that the
existence of divide-and-conquer algorithms makes our new approach a good
candidate for a practical implementation
Electrical Flows, Laplacian Systems, and Faster Approximation of Maximum Flow in Undirected Graphs
We introduce a new approach to computing an approximately maximum s-t flow in
a capacitated, undirected graph. This flow is computed by solving a sequence of
electrical flow problems. Each electrical flow is given by the solution of a
system of linear equations in a Laplacian matrix, and thus may be approximately
computed in nearly-linear time.
Using this approach, we develop the fastest known algorithm for computing
approximately maximum s-t flows. For a graph having n vertices and m edges, our
algorithm computes a (1-\epsilon)-approximately maximum s-t flow in time
\tilde{O}(mn^{1/3} \epsilon^{-11/3}). A dual version of our approach computes a
(1+\epsilon)-approximately minimum s-t cut in time
\tilde{O}(m+n^{4/3}\eps^{-8/3}), which is the fastest known algorithm for this
problem as well. Previously, the best dependence on m and n was achieved by the
algorithm of Goldberg and Rao (J. ACM 1998), which can be used to compute
approximately maximum s-t flows in time \tilde{O}(m\sqrt{n}\epsilon^{-1}), and
approximately minimum s-t cuts in time \tilde{O}(m+n^{3/2}\epsilon^{-3})
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