45,454 research outputs found
Finding Densest -Connected Subgraphs
Dense subgraph discovery is an important graph-mining primitive with a
variety of real-world applications. One of the most well-studied optimization
problems for dense subgraph discovery is the densest subgraph problem, where
given an edge-weighted undirected graph , we are asked to find
that maximizes the density , i.e., half the weighted
average degree of the induced subgraph . This problem can be solved
exactly in polynomial time and well-approximately in almost linear time.
However, a densest subgraph has a structural drawback, namely, the subgraph may
not be robust to vertex/edge failure. Indeed, a densest subgraph may not be
well-connected, which implies that the subgraph may be disconnected by removing
only a few vertices/edges within it. In this paper, we provide an algorithmic
framework to find a dense subgraph that is well-connected in terms of
vertex/edge connectivity. Specifically, we introduce the following problems:
given a graph and a positive integer/real , we are asked to find
that maximizes the density under the constraint that
is -vertex/edge-connected. For both problems, we propose
polynomial-time (bicriteria and ordinary) approximation algorithms, using
classic Mader's theorem in graph theory and its extensions
Some hard families of parameterised counting problems
We consider parameterised subgraph-counting problems of the following form:
given a graph G, how many k-tuples of its vertices have a given property? A
number of such problems are known to be #W[1]-complete; here we substantially
generalise some of these existing results by proving hardness for two large
families of such problems. We demonstrate that it is #W[1]-hard to count the
number of k-vertex subgraphs having any property where the number of distinct
edge-densities of labelled subgraphs that satisfy the property is o(k^2). In
the special case that the property in question depends only on the number of
edges in the subgraph, we give a strengthening of this result which leads to
our second family of hard problems.Comment: A few more minor changes. This version to appear in the ACM
Transactions on Computation Theor
Approximating minimum power covers of intersecting families and directed edge-connectivity problems
AbstractGiven a (directed) graph with costs on the edges, the power of a node is the maximum cost of an edge leaving it, and the power of the graph is the sum of the powers of its nodes. Let G=(V,E) be a graph with edge costs {c(e):e∈E} and let k be an integer. We consider problems that seek to find a min-power spanning subgraph G of G that satisfies a prescribed edge-connectivity property. In the Min-Powerk-Edge-Outconnected Subgraph problem we are given a root r∈V, and require that G contains k pairwise edge-disjoint rv-paths for all v∈V−r. In the Min-Powerk-Edge-Connected Subgraph problem G is required to be k-edge-connected. For k=1, these problems are at least as hard as the Set-Cover problem and thus have an Ω(ln|V|) approximation threshold. For k=Ω(nε), they are unlikely to admit a polylogarithmic approximation ratio [15]. We give approximation algorithms with ratio O(kln|V|). Our algorithms are based on a more general O(ln|V|)-approximation algorithm for the problem of finding a min-power directed edge-cover of an intersecting set-family; a set-family F is intersecting if X∩Y,X∪Y∈F for any intersecting X,Y∈F, and an edge set I covers F if for every X∈F there is an edge in I entering X
Parameterized Algorithms for Graph Partitioning Problems
We study a broad class of graph partitioning problems, where each problem is
specified by a graph , and parameters and . We seek a subset
of size , such that is at most
(or at least) , where are constants
defining the problem, and are the cardinalities of the edge sets
having both endpoints, and exactly one endpoint, in , respectively. This
class of fixed cardinality graph partitioning problems (FGPP) encompasses Max
-Cut, Min -Vertex Cover, -Densest Subgraph, and -Sparsest
Subgraph.
Our main result is an algorithm for any problem in
this class, where is the maximum degree in the input graph.
This resolves an open question posed by Bonnet et al. [IPEC 2013]. We obtain
faster algorithms for certain subclasses of FGPPs, parameterized by , or by
. In particular, we give an time algorithm for Max
-Cut, thus improving significantly the best known time
algorithm
Approximating minimum cost connectivity problems
We survey approximation algorithms of connectivity problems.
The survey presented describing various techniques. In the talk the following techniques and results are presented.
1)Outconnectivity: Its well known that there exists a polynomial time algorithm to solve the problems of finding an edge k-outconnected from r subgraph [EDMONDS] and a vertex k-outconnectivity subgraph from r [Frank-Tardos] .
We show how to use this to obtain a ratio 2 approximation for the min cost edge k-connectivity
problem.
2)The critical cycle theorem of Mader: We state a fundamental theorem of Mader and use it to provide a 1+(k-1)/n ratio approximation for the min cost vertex k-connected subgraph, in the metric case.
We also show results for the min power vertex k-connected problem using this lemma.
We show that the min power is equivalent to the min-cost case with respect to approximation.
3)Laminarity and uncrossing: We use the well known laminarity of a BFS solution and show a simple new proof due to Ravi et al for Jain\u27s 2 approximation for Steiner network
Inapproximability of Maximum Biclique Problems, Minimum -Cut and Densest At-Least--Subgraph from the Small Set Expansion Hypothesis
The Small Set Expansion Hypothesis (SSEH) is a conjecture which roughly
states that it is NP-hard to distinguish between a graph with a small subset of
vertices whose edge expansion is almost zero and one in which all small subsets
of vertices have expansion almost one. In this work, we prove inapproximability
results for the following graph problems based on this hypothesis:
- Maximum Edge Biclique (MEB): given a bipartite graph , find a complete
bipartite subgraph of with maximum number of edges.
- Maximum Balanced Biclique (MBB): given a bipartite graph , find a
balanced complete bipartite subgraph of with maximum number of vertices.
- Minimum -Cut: given a weighted graph , find a set of edges with
minimum total weight whose removal partitions into connected
components.
- Densest At-Least--Subgraph (DALS): given a weighted graph , find a
set of at least vertices such that the induced subgraph on has
maximum density (the ratio between the total weight of edges and the number of
vertices).
We show that, assuming SSEH and NP BPP, no polynomial time
algorithm gives -approximation for MEB or MBB for every
constant . Moreover, assuming SSEH, we show that it is NP-hard
to approximate Minimum -Cut and DALS to within factor
of the optimum for every constant .
The ratios in our results are essentially tight since trivial algorithms give
-approximation to both MEB and MBB and efficient -approximation
algorithms are known for Minimum -Cut [SV95] and DALS [And07, KS09].
Our first result is proved by combining a technique developed by Raghavendra
et al. [RST12] to avoid locality of gadget reductions with a generalization of
Bansal and Khot's long code test [BK09] whereas our second result is shown via
elementary reductions.Comment: A preliminary version of this work will appear at ICALP 2017 under a
different title "Inapproximability of Maximum Edge Biclique, Maximum Balanced
Biclique and Minimum k-Cut from the Small Set Expansion Hypothesis
Approximating minimum-power edge-multicovers
Given a graph with edge costs, the {\em power} of a node is themaximum cost
of an edge incident to it, and the power of a graph is the sum of the powers of
its nodes. Motivated by applications in wireless networks, we consider the
following fundamental problem in wireless network design. Given a graph
with edge costs and degree bounds , the {\sf
Minimum-Power Edge-Multi-Cover} ({\sf MPEMC}) problem is to find a
minimum-power subgraph of such that the degree of every node in
is at least . We give two approximation algorithms for {\sf MPEMC}, with
ratios and , where is the maximum
degree bound. This improves the previous ratios and , and
implies ratios for the {\sf Minimum-Power -Outconnected
Subgraph} and for the {\sf Minimum-Power
-Connected Subgraph} problems; the latter is the currently best known ratio
for the min-cost version of the problem
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