60,666 research outputs found
Predicting epidemic evolution on contact networks from partial observations
The massive employment of computational models in network epidemiology calls
for the development of improved inference methods for epidemic forecast. For
simple compartment models, such as the Susceptible-Infected-Recovered model,
Belief Propagation was proved to be a reliable and efficient method to identify
the origin of an observed epidemics. Here we show that the same method can be
applied to predict the future evolution of an epidemic outbreak from partial
observations at the early stage of the dynamics. The results obtained using
Belief Propagation are compared with Monte Carlo direct sampling in the case of
SIR model on random (regular and power-law) graphs for different observation
methods and on an example of real-world contact network. Belief Propagation
gives in general a better prediction that direct sampling, although the quality
of the prediction depends on the quantity under study (e.g. marginals of
individual states, epidemic size, extinction-time distribution) and on the
actual number of observed nodes that are infected before the observation time
Synchronization in random networks with given expected degree sequences
Synchronization in random networks with given expected degree sequences is studied. We also investigate in details the synchronization in networks whose topology is described by classical random graphs, power-law random graphs and hybrid graphs when N goes to infinity. In particular, we show that random graphs almost surely synchronize. We also show that adding small number of global edges to a local graph makes the corresponding hybrid graph to synchroniz
Chinese Internet AS-level Topology
We present the first complete measurement of the Chinese Internet topology at
the autonomous systems (AS) level based on traceroute data probed from servers
of major ISPs in mainland China. We show that both the Chinese Internet AS
graph and the global Internet AS graph can be accurately reproduced by the
Positive-Feedback Preference (PFP) model with the same parameters. This result
suggests that the Chinese Internet preserves well the topological
characteristics of the global Internet. This is the first demonstration of the
Internet's topological fractality, or self-similarity, performed at the level
of topology evolution modeling.Comment: This paper is a preprint of a paper submitted to IEE Proceedings on
Communications and is subject to Institution of Engineering and Technology
Copyright. If accepted, the copy of record will be available at IET Digital
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Network Structure Mining and Evolution Analysis - Based on BA Scale-Free Network Model
The massive adoption of the Internet facilitates growth of online social networks, in which information can be exchanged in a more efficient way. Such as products, user accounts, web pages, there may be a variety of objects suitable to structurize this kind of networks. As a result, this gives the networks complexity and dynamics. The work in this paper is aiming to studying the topological property of online social network structure from the aspect of dynamics, and make clear the evolution processes of the networks. This is done by a Mean-Field analysis of network growth based on BA Scale-Free network model. Data resources come from the Chinese online e-commerce platform you.163.com and graphs are modeled through commentator and mutual comments by calculating degree distribution of the networks. We build a growing random model for forecasting dynamics of degree evolution. Finally, we use data set on Sina Weibo to test the model and the results are satisfying
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