571 research outputs found
Towards Optimal Degree-distributions for Left-perfect Matchings in Random Bipartite Graphs
Consider a random bipartite multigraph with left nodes and right nodes. Each left node has random right
neighbors. The average left degree is fixed, . We ask
whether for the probability that has a left-perfect matching it is
advantageous not to fix for each left node but rather choose it at
random according to some (cleverly chosen) distribution. We show the following,
provided that the degrees of the left nodes are independent: If is an
integer then it is optimal to use a fixed degree of for all left
nodes. If is non-integral then an optimal degree-distribution has the
property that each left node has two possible degrees, \floor{\Delta} and
\ceil{\Delta}, with probability and , respectively, where
is from the closed interval and the average over all equals
\ceil{\Delta}-\Delta. Furthermore, if and is
constant, then each distribution of the left degrees that meets the conditions
above determines the same threshold that has the following
property as goes to infinity: If then there exists a
left-perfect matching with high probability. If then there
exists no left-perfect matching with high probability. The threshold
is the same as the known threshold for offline -ary cuckoo
hashing for integral or non-integral
Approximately Counting Triangles in Sublinear Time
We consider the problem of estimating the number of triangles in a graph.
This problem has been extensively studied in both theory and practice, but all
existing algorithms read the entire graph. In this work we design a {\em
sublinear-time\/} algorithm for approximating the number of triangles in a
graph, where the algorithm is given query access to the graph. The allowed
queries are degree queries, vertex-pair queries and neighbor queries.
We show that for any given approximation parameter , the
algorithm provides an estimate such that with high constant
probability, , where
is the number of triangles in the graph . The expected query complexity of
the algorithm is , where
is the number of vertices in the graph and is the number of edges, and
the expected running time is . We also prove
that queries are necessary, thus establishing that
the query complexity of this algorithm is optimal up to polylogarithmic factors
in (and the dependence on ).Comment: To appear in the 56th Annual IEEE Symposium on Foundations of
Computer Science (FOCS 2015
Algorithm and Complexity for a Network Assortativity Measure
We show that finding a graph realization with the minimum Randi\'c index for
a given degree sequence is solvable in polynomial time by formulating the
problem as a minimum weight perfect b-matching problem. However, the
realization found via this reduction is not guaranteed to be connected.
Approximating the minimum weight b-matching problem subject to a connectivity
constraint is shown to be NP-Hard. For instances in which the optimal solution
to the minimum Randi\'c index problem is not connected, we describe a heuristic
to connect the graph using pairwise edge exchanges that preserves the degree
sequence. In our computational experiments, the heuristic performs well and the
Randi\'c index of the realization after our heuristic is within 3% of the
unconstrained optimal value on average. Although we focus on minimizing the
Randi\'c index, our results extend to maximizing the Randi\'c index as well.
Applications of the Randi\'c index to synchronization of neuronal networks
controlling respiration in mammals and to normalizing cortical thickness
networks in diagnosing individuals with dementia are provided.Comment: Added additional section on application
Resilient degree sequences with respect to Hamilton cycles and matchings in random graphs
P\'osa's theorem states that any graph whose degree sequence satisfies for all has a Hamilton cycle.
This degree condition is best possible. We show that a similar result holds for
suitable subgraphs of random graphs, i.e. we prove a `resilience version'
of P\'osa's theorem: if and the -th vertex degree (ordered
increasingly) of is at least for all ,
then has a Hamilton cycle. This is essentially best possible and
strengthens a resilience version of Dirac's theorem obtained by Lee and
Sudakov.
Chv\'atal's theorem generalises P\'osa's theorem and characterises all degree
sequences which ensure the existence of a Hamilton cycle. We show that a
natural guess for a resilience version of Chv\'atal's theorem fails to be true.
We formulate a conjecture which would repair this guess, and show that the
corresponding degree conditions ensure the existence of a perfect matching in
any subgraph of which satisfies these conditions. This provides an
asymptotic characterisation of all degree sequences which resiliently guarantee
the existence of a perfect matching.Comment: To appear in the Electronic Journal of Combinatorics. This version
corrects a couple of typo
A No-Go Theorem for Derandomized Parallel Repetition: Beyond Feige-Kilian
In this work we show a barrier towards proving a randomness-efficient
parallel repetition, a promising avenue for achieving many tight
inapproximability results. Feige and Kilian (STOC'95) proved an impossibility
result for randomness-efficient parallel repetition for two prover games with
small degree, i.e., when each prover has only few possibilities for the
question of the other prover. In recent years, there have been indications that
randomness-efficient parallel repetition (also called derandomized parallel
repetition) might be possible for games with large degree, circumventing the
impossibility result of Feige and Kilian. In particular, Dinur and Meir
(CCC'11) construct games with large degree whose repetition can be derandomized
using a theorem of Impagliazzo, Kabanets and Wigderson (SICOMP'12). However,
obtaining derandomized parallel repetition theorems that would yield optimal
inapproximability results has remained elusive.
This paper presents an explanation for the current impasse in progress, by
proving a limitation on derandomized parallel repetition. We formalize two
properties which we call "fortification-friendliness" and "yields robust
embeddings." We show that any proof of derandomized parallel repetition
achieving almost-linear blow-up cannot both (a) be fortification-friendly and
(b) yield robust embeddings. Unlike Feige and Kilian, we do not require the
small degree assumption.
Given that virtually all existing proofs of parallel repetition, including
the derandomized parallel repetition result of Dinur and Meir, share these two
properties, our no-go theorem highlights a major barrier to achieving
almost-linear derandomized parallel repetition
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