80,976 research outputs found
The Naming Game in Social Networks: Community Formation and Consensus Engineering
We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.
Mech.: Theory Exp. P06014] in empirical social networks. This stylized
agent-based model captures essential features of agreement dynamics in a
network of autonomous agents, corresponding to the development of shared
classification schemes in a network of artificial agents or opinion spreading
and social dynamics in social networks. Our study focuses on the impact that
communities in the underlying social graphs have on the outcome of the
agreement process. We find that networks with strong community structure hinder
the system from reaching global agreement; the evolution of the Naming Game in
these networks maintains clusters of coexisting opinions indefinitely. Further,
we investigate agent-based network strategies to facilitate convergence to
global consensus.Comment: The original publication is available at
http://www.springerlink.com/content/70370l311m1u0ng3
Decelerated spreading in degree-correlated networks
While degree correlations are known to play a crucial role for spreading
phenomena in networks, their impact on the propagation speed has hardly been
understood. Here we investigate a tunable spreading model on scale-free
networks and show that the propagation becomes slow in positively (negatively)
correlated networks if nodes with a high connectivity locally accelerate
(decelerate) the propagation. Examining the efficient paths offers a coherent
explanation for this result, while the -core decomposition reveals the
dependence of the nodal spreading efficiency on the correlation. Our findings
should open new pathways to delicately control real-world spreading processes
Storage and Search in Dynamic Peer-to-Peer Networks
We study robust and efficient distributed algorithms for searching, storing,
and maintaining data in dynamic Peer-to-Peer (P2P) networks. P2P networks are
highly dynamic networks that experience heavy node churn (i.e., nodes join and
leave the network continuously over time). Our goal is to guarantee, despite
high node churn rate, that a large number of nodes in the network can store,
retrieve, and maintain a large number of data items. Our main contributions are
fast randomized distributed algorithms that guarantee the above with high
probability (whp) even under high adversarial churn:
1. A randomized distributed search algorithm that (whp) guarantees that
searches from as many as nodes ( is the stable network size)
succeed in -rounds despite churn, for
any small constant , per round. We assume that the churn is
controlled by an oblivious adversary (that has complete knowledge and control
of what nodes join and leave and at what time, but is oblivious to the random
choices made by the algorithm).
2. A storage and maintenance algorithm that guarantees (whp) data items can
be efficiently stored (with only copies of each data item)
and maintained in a dynamic P2P network with churn rate up to
per round. Our search algorithm together with our
storage and maintenance algorithm guarantees that as many as nodes
can efficiently store, maintain, and search even under churn per round. Our algorithms require only polylogarithmic in bits to
be processed and sent (per round) by each node.
To the best of our knowledge, our algorithms are the first-known,
fully-distributed storage and search algorithms that provably work under highly
dynamic settings (i.e., high churn rates per step).Comment: to appear at SPAA 201
Statistical analysis of articulation points in configuration model networks
An articulation point (AP) in a network is a node whose deletion would split
the network component on which it resides into two or more components. APs are
vulnerable spots that play an important role in network collapse processes,
which may result from node failures, attacks or epidemics. Therefore, the
abundance and properties of APs affect the resilience of the network to these
collapse scenarios. We present analytical results for the statistical
properties of APs in configuration model networks. In order to quantify their
abundance, we calculate the probability , that a random
node, i, in a configuration model network with P(K=k), is an AP. We also obtain
the conditional probability that a random node of degree
k is an AP, and find that high degree nodes are more likely to be APs than low
degree nodes. Using Bayes' theorem, we obtain the conditional degree
distribution, , over the set of APs and compare it to P(K=k).
We propose a new centrality measure based on APs: each node can be
characterized by its articulation rank, r, which is the number of components
that would be added to the network upon deletion of that node. For nodes which
are not APs the articulation rank is , while for APs . We obtain
a closed form expression for the distribution of articulation ranks, P(R=r).
Configuration model networks often exhibit a coexistence between a giant
component and finite components. To examine the distinct properties of APs on
the giant and on the finite components, we calculate the probabilities
presented above separately for the giant and the finite components. We apply
these results to ensembles of configuration model networks with a Poisson,
exponential and power-law degree distributions. The implications of these
results are discussed in the context of common attack scenarios and network
dismantling processes.Comment: 53 pages, 16 figures. arXiv admin note: text overlap with
arXiv:1804.0333
Asymptotic analysis of first passage time in complex networks
The first passage time (FPT) distribution for random walk in complex networks
is calculated through an asymptotic analysis. For network with size and
short relaxation time , the computed mean first passage time (MFPT),
which is inverse of the decay rate of FPT distribution, is inversely
proportional to the degree of the destination. These results are verified
numerically for the paradigmatic networks with excellent agreement. We show
that the range of validity of the analytical results covers networks that have
short relaxation time and high mean degree, which turn out to be valid to many
real networks.Comment: 6 pages, 4 figures, 1 tabl
Survey of Distributed Decision
We survey the recent distributed computing literature on checking whether a
given distributed system configuration satisfies a given boolean predicate,
i.e., whether the configuration is legal or illegal w.r.t. that predicate. We
consider classical distributed computing environments, including mostly
synchronous fault-free network computing (LOCAL and CONGEST models), but also
asynchronous crash-prone shared-memory computing (WAIT-FREE model), and mobile
computing (FSYNC model)
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
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