11,972 research outputs found
Self-stabilizing algorithms for Connected Vertex Cover and Clique decomposition problems
In many wireless networks, there is no fixed physical backbone nor
centralized network management. The nodes of such a network have to
self-organize in order to maintain a virtual backbone used to route messages.
Moreover, any node of the network can be a priori at the origin of a malicious
attack. Thus, in one hand the backbone must be fault-tolerant and in other hand
it can be useful to monitor all network communications to identify an attack as
soon as possible. We are interested in the minimum \emph{Connected Vertex
Cover} problem, a generalization of the classical minimum Vertex Cover problem,
which allows to obtain a connected backbone. Recently, Delbot et
al.~\cite{DelbotLP13} proposed a new centralized algorithm with a constant
approximation ratio of for this problem. In this paper, we propose a
distributed and self-stabilizing version of their algorithm with the same
approximation guarantee. To the best knowledge of the authors, it is the first
distributed and fault-tolerant algorithm for this problem. The approach
followed to solve the considered problem is based on the construction of a
connected minimal clique partition. Therefore, we also design the first
distributed self-stabilizing algorithm for this problem, which is of
independent interest
Optimal Dynamic Distributed MIS
Finding a maximal independent set (MIS) in a graph is a cornerstone task in
distributed computing. The local nature of an MIS allows for fast solutions in
a static distributed setting, which are logarithmic in the number of nodes or
in their degrees. The result trivially applies for the dynamic distributed
model, in which edges or nodes may be inserted or deleted. In this paper, we
take a different approach which exploits locality to the extreme, and show how
to update an MIS in a dynamic distributed setting, either \emph{synchronous} or
\emph{asynchronous}, with only \emph{a single adjustment} and in a single
round, in expectation. These strong guarantees hold for the \emph{complete
fully dynamic} setting: Insertions and deletions, of edges as well as nodes,
gracefully and abruptly. This strongly separates the static and dynamic
distributed models, as super-constant lower bounds exist for computing an MIS
in the former.
Our results are obtained by a novel analysis of the surprisingly simple
solution of carefully simulating the greedy \emph{sequential} MIS algorithm
with a random ordering of the nodes. As such, our algorithm has a direct
application as a -approximation algorithm for correlation clustering. This
adds to the important toolbox of distributed graph decompositions, which are
widely used as crucial building blocks in distributed computing.
Finally, our algorithm enjoys a useful \emph{history-independence} property,
meaning the output is independent of the history of topology changes that
constructed that graph. This means the output cannot be chosen, or even biased,
by the adversary in case its goal is to prevent us from optimizing some
objective function.Comment: 19 pages including appendix and reference
The network of stabilizing contacts in proteins studied by coevolutionary data
The primary structure of proteins, that is their sequence, represents one of
the most abundant set of experimental data concerning biomolecules. The study
of correlations in families of co--evolving proteins by means of an inverse
Ising--model approach allows to obtain information on their native
conformation. Following up on a recent development along this line, we optimize
the algorithm to calculate effective energies between the residues, validating
the approach both back-calculating interaction energies in a model system, and
predicting the free energies associated to mutations in real systems. Making
use of these effective energies, we study the networks of interactions which
stabilizes the native conformation of some well--studied proteins, showing that
it display different properties than the associated contact network
Improving Connectionist Energy Minimization
Symmetric networks designed for energy minimization such as Boltzman machines
and Hopfield nets are frequently investigated for use in optimization,
constraint satisfaction and approximation of NP-hard problems. Nevertheless,
finding a global solution (i.e., a global minimum for the energy function) is
not guaranteed and even a local solution may take an exponential number of
steps. We propose an improvement to the standard local activation function used
for such networks. The improved algorithm guarantees that a global minimum is
found in linear time for tree-like subnetworks. The algorithm, called activate,
is uniform and does not assume that the network is tree-like. It can identify
tree-like subnetworks even in cyclic topologies (arbitrary networks) and avoid
local minima along these trees. For acyclic networks, the algorithm is
guaranteed to converge to a global minimum from any initial state of the system
(self-stabilization) and remains correct under various types of schedulers. On
the negative side, we show that in the presence of cycles, no uniform algorithm
exists that guarantees optimality even under a sequential asynchronous
scheduler. An asynchronous scheduler can activate only one unit at a time while
a synchronous scheduler can activate any number of units in a single time step.
In addition, no uniform algorithm exists to optimize even acyclic networks when
the scheduler is synchronous. Finally, we show how the algorithm can be
improved using the cycle-cutset scheme. The general algorithm, called
activate-with-cutset, improves over activate and has some performance
guarantees that are related to the size of the network's cycle-cutset.Comment: See http://www.jair.org/ for any accompanying file
The Evolution of Wikipedia's Norm Network
Social norms have traditionally been difficult to quantify. In any particular
society, their sheer number and complex interdependencies often limit a
system-level analysis. One exception is that of the network of norms that
sustain the online Wikipedia community. We study the fifteen-year evolution of
this network using the interconnected set of pages that establish, describe,
and interpret the community's norms. Despite Wikipedia's reputation for
\textit{ad hoc} governance, we find that its normative evolution is highly
conservative. The earliest users create norms that both dominate the network
and persist over time. These core norms govern both content and interpersonal
interactions using abstract principles such as neutrality, verifiability, and
assume good faith. As the network grows, norm neighborhoods decouple
topologically from each other, while increasing in semantic coherence. Taken
together, these results suggest that the evolution of Wikipedia's norm network
is akin to bureaucratic systems that predate the information age.Comment: 22 pages, 9 figures. Matches published version. Data available at
http://bit.ly/wiki_nor
A Self-Stabilizing K-Clustering Algorithm Using an Arbitrary Metric (Revised Version of RR2008-31)
32 pagesMobile ad hoc networks as well as grid platforms are distributed, changing, and error prone environments. Communication costs within such infrastructure can be improved, or at least bounded, by using k-clustering. A k-clustering of a graph, is a partition of the nodes into disjoint sets, called clusters, in which every node is distance at most k from a designated node in its cluster, called the clusterhead. A self-stabilizing asynchronous distributed algorithm is given for constructing a k-clustering of a connected network of processes with unique IDs and weighted edges. The algorithm is comparison-based, takes O(nk) time, and uses O(log n + log k) space per process, where n is the size of the network. This is the first distributed solution to the k-clustering problem on weighted graphs
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