50,227 research outputs found
K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases
We consider the -core decomposition of network models and Internet graphs
at the autonomous system (AS) level. The -core analysis allows to
characterize networks beyond the degree distribution and uncover structural
properties and hierarchies due to the specific architecture of the system. We
compare the -core structure obtained for AS graphs with those of several
network models and discuss the differences and similarities with the real
Internet architecture. The presence of biases and the incompleteness of the
real maps are discussed and their effect on the -core analysis is assessed
with numerical experiments simulating biased exploration on a wide range of
network models. We find that the -core analysis provides an interesting
characterization of the fluctuations and incompleteness of maps as well as
information helping to discriminate the original underlying structure
Exploring networks with traceroute-like probes: theory and simulations
Mapping the Internet generally consists in sampling the network from a
limited set of sources by using traceroute-like probes. This methodology, akin
to the merging of different spanning trees to a set of destination, has been
argued to introduce uncontrolled sampling biases that might produce statistical
properties of the sampled graph which sharply differ from the original ones. In
this paper we explore these biases and provide a statistical analysis of their
origin. We derive an analytical approximation for the probability of edge and
vertex detection that exploits the role of the number of sources and targets
and allows us to relate the global topological properties of the underlying
network with the statistical accuracy of the sampled graph. In particular, we
find that the edge and vertex detection probability depends on the betweenness
centrality of each element. This allows us to show that shortest path routed
sampling provides a better characterization of underlying graphs with broad
distributions of connectivity. We complement the analytical discussion with a
throughout numerical investigation of simulated mapping strategies in network
models with different topologies. We show that sampled graphs provide a fair
qualitative characterization of the statistical properties of the original
networks in a fair range of different strategies and exploration parameters.
Moreover, we characterize the level of redundancy and completeness of the
exploration process as a function of the topological properties of the network.
Finally, we study numerically how the fraction of vertices and edges discovered
in the sampled graph depends on the particular deployements of probing sources.
The results might hint the steps toward more efficient mapping strategies.Comment: This paper is related to cond-mat/0406404, with explorations of
different networks and complementary discussion
A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial
networks, borrowing from style transfer literature. The new architecture leads
to an automatically learned, unsupervised separation of high-level attributes
(e.g., pose and identity when trained on human faces) and stochastic variation
in the generated images (e.g., freckles, hair), and it enables intuitive,
scale-specific control of the synthesis. The new generator improves the
state-of-the-art in terms of traditional distribution quality metrics, leads to
demonstrably better interpolation properties, and also better disentangles the
latent factors of variation. To quantify interpolation quality and
disentanglement, we propose two new, automated methods that are applicable to
any generator architecture. Finally, we introduce a new, highly varied and
high-quality dataset of human faces.Comment: CVPR 2019 final versio
The Internet AS-Level Topology: Three Data Sources and One Definitive Metric
We calculate an extensive set of characteristics for Internet AS topologies
extracted from the three data sources most frequently used by the research
community: traceroutes, BGP, and WHOIS. We discover that traceroute and BGP
topologies are similar to one another but differ substantially from the WHOIS
topology. Among the widely considered metrics, we find that the joint degree
distribution appears to fundamentally characterize Internet AS topologies as
well as narrowly define values for other important metrics. We discuss the
interplay between the specifics of the three data collection mechanisms and the
resulting topology views. In particular, we show how the data collection
peculiarities explain differences in the resulting joint degree distributions
of the respective topologies. Finally, we release to the community the input
topology datasets, along with the scripts and output of our calculations. This
supplement should enable researchers to validate their models against real data
and to make more informed selection of topology data sources for their specific
needs.Comment: This paper is a revised journal version of cs.NI/050803
Bias estimation in sensor networks
This paper investigates the problem of estimating biases affecting relative
state measurements in a sensor network. Each sensor measures the relative
states of its neighbors and this measurement is corrupted by a constant bias.
We analyse under what conditions on the network topology and the maximum number
of biased sensors the biases can be correctly estimated. We show that for
non-bipartite graphs the biases can always be determined even when all the
sensors are corrupted, while for bipartite graphs more than half of the sensors
should be unbiased to ensure the correctness of the bias estimation. If the
biases are heterogeneous, then the number of unbiased sensors can be reduced to
two. Based on these conditions, we propose some algorithms to estimate the
biases.Comment: 12 pages, 8 figure
- …