7,907 research outputs found
Analysis of Push-type Epidemic Data Dissemination in Fully Connected Networks
Consider a fully connected network of nodes, some of which have a piece of
data to be disseminated to the whole network. We analyze the following
push-type epidemic algorithm: in each push round, every node that has the data,
i.e., every infected node, randomly chooses other nodes
in the network and transmits, i.e., pushes, the data to them. We write this
round as a random walk whose each step corresponds to a random selection of one
of the infected nodes; this gives recursive formulas for the distribution and
the moments of the number of newly infected nodes in a push round. We use the
formula for the distribution to compute the expected number of rounds so that a
given percentage of the network is infected and continue a numerical comparison
of the push algorithm and the pull algorithm (where the susceptible nodes
randomly choose peers) initiated in an earlier work. We then derive the fluid
and diffusion limits of the random walk as the network size goes to
and deduce a number of properties of the push algorithm: 1) the number of newly
infected nodes in a push round, and the number of random selections needed so
that a given percent of the network is infected, are both asymptotically normal
2) for large networks, starting with a nonzero proportion of infected nodes, a
pull round infects slightly more nodes on average 3) the number of rounds until
a given proportion of the network is infected converges to a constant
for almost all . Numerical examples for theoretical results
are provided.Comment: 28 pages, 5 figure
Centrality Measures for Networks with Community Structure
Understanding the network structure, and finding out the influential nodes is
a challenging issue in the large networks. Identifying the most influential
nodes in the network can be useful in many applications like immunization of
nodes in case of epidemic spreading, during intentional attacks on complex
networks. A lot of research is done to devise centrality measures which could
efficiently identify the most influential nodes in the network. There are two
major approaches to the problem: On one hand, deterministic strategies that
exploit knowledge about the overall network topology in order to find the
influential nodes, while on the other end, random strategies are completely
agnostic about the network structure. Centrality measures that can deal with a
limited knowledge of the network structure are required. Indeed, in practice,
information about the global structure of the overall network is rarely
available or hard to acquire. Even if available, the structure of the network
might be too large that it is too much computationally expensive to calculate
global centrality measures. To that end, a centrality measure is proposed that
requires information only at the community level to identify the influential
nodes in the network. Indeed, most of the real-world networks exhibit a
community structure that can be exploited efficiently to discover the
influential nodes. We performed a comparative evaluation of prominent global
deterministic strategies together with stochastic strategies with an available
and the proposed deterministic community-based strategy. Effectiveness of the
proposed method is evaluated by performing experiments on synthetic and
real-world networks with community structure in the case of immunization of
nodes for epidemic control.Comment: 30 pages, 4 figures. Accepted for publication in Physica A. arXiv
admin note: text overlap with arXiv:1411.627
The structure and function of complex networks
Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.Comment: Review article, 58 pages, 16 figures, 3 tables, 429 references,
published in SIAM Review (2003
Domino: exploring mobile collaborative software adaptation
Social Proximity Applications (SPAs) are a promising new area for ubicomp software that exploits the everyday changes in the proximity of mobile users. While a number of applications facilitate simple file sharing between co–present users, this paper explores opportunities for recommending and sharing software between users. We describe an architecture that allows the recommendation of new system components from systems with similar histories of use. Software components and usage histories are exchanged between mobile users who are in proximity with each other. We apply this architecture in a mobile strategy game in which players adapt and upgrade their game using components from other players, progressing through the game through sharing tools and history. More broadly, we discuss the general application of this technique as well as the security and privacy challenges to such an approach
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