446 research outputs found
Long-Range Correlations and Memory in the Dynamics of Internet Interdomain Routing
Data transfer is one of the main functions of the Internet. The Internet
consists of a large number of interconnected subnetworks or domains, known as
Autonomous Systems. Due to privacy and other reasons the information about what
route to use to reach devices within other Autonomous Systems is not readily
available to any given Autonomous System. The Border Gateway Protocol is
responsible for discovering and distributing this reachability information to
all Autonomous Systems. Since the topology of the Internet is highly dynamic,
all Autonomous Systems constantly exchange and update this reachability
information in small chunks, known as routing control packets or Border Gateway
Protocol updates. Motivated by scalability and predictability issues with the
dynamics of these updates in the quickly growing Internet, we conduct a
systematic time series analysis of Border Gateway Protocol update rates. We
find that Border Gateway Protocol update time series are extremely volatile,
exhibit long-term correlations and memory effects, similar to seismic time
series, or temperature and stock market price fluctuations. The presented
statistical characterization of Border Gateway Protocol update dynamics could
serve as a ground truth for validation of existing and developing better models
of Internet interdomain routing
Latent Geometry for Complementarity-Driven Networks
Networks of interdisciplinary teams, biological interactions as well as food
webs are examples of networks that are shaped by complementarity principles:
connections in these networks are preferentially established between nodes with
complementary properties. We propose a geometric framework for
complementarity-driven networks. In doing so we first argue that traditional
geometric representations, e.g., embeddings of networks into latent metric
spaces, are not applicable to complementarity-driven networks due to the
contradiction between the triangle inequality in latent metric spaces and the
non-transitivity of complementarity. We then propose the cross-geometric
representation for these complementarity-driven networks and demonstrate that
this representation (i) follows naturally from the complementarity rule, (ii)
is consistent with the metric property of the latent space, (iii) reproduces
structural properties of real complementarity-driven networks, if the latent
space is the hyperbolic disk, and (iv) allows for prediction of missing links
in complementarity-driven networks with accuracy surpassing existing
similarity-based methods. The proposed framework challenges social network
analysis intuition and tools that are routinely applied to
complementarity-driven networks and offers new avenues towards descriptive and
prescriptive analysis of systems in science of science and biomedicine
Structure of Business Firm Networks and Scale-Free Models
We study the structure of business firm networks and scale-free models with
degree distribution using the method of
-shell decomposition.We find that the Life Sciences industry network consist
of three components: a ``nucleus,'' which is a small well connected subgraph,
``tendrils,'' which are small subgraphs consisting of small degree nodes
connected exclusively to the nucleus, and a ``bulk body'' which consists of the
majority of nodes. At the same time we do not observe the above structure in
the Information and Communication Technology sector of industry. We also
conduct a systematic study of these three components in random scale-free
networks. Our results suggest that the sizes of the nucleus and the tendrils
decrease as increases and disappear for . We compare
the -shell structure of random scale-free model networks with two real world
business firm networks in the Life Sciences and in the Information and
Communication Technology sectors. Our results suggest that the observed
behavior of the -shell structure in the two industries is consistent with a
recently proposed growth model that assumes the coexistence of both
preferential and random agreements in the evolution of industrial networks
Seed selection for information cascade in multilayer networks
Information spreading is an interesting field in the domain of online social
media. In this work, we are investigating how well different seed selection
strategies affect the spreading processes simulated using independent cascade
model on eighteen multilayer social networks. Fifteen networks are built based
on the user interaction data extracted from Facebook public pages and tree of
them are multilayer networks downloaded from public repository (two of them
being Twitter networks). The results indicate that various state of the art
seed selection strategies for single-layer networks like K-Shell or VoteRank do
not perform so well on multilayer networks and are outperformed by Degree
Centrality
Betweenness Centrality of Fractal and Non-Fractal Scale-Free Model Networks and Tests on Real Networks
We study the betweenness centrality of fractal and non-fractal scale-free
network models as well as real networks. We show that the correlation between
degree and betweenness centrality of nodes is much weaker in fractal
network models compared to non-fractal models. We also show that nodes of both
fractal and non-fractal scale-free networks have power law betweenness
centrality distribution . We find that for non-fractal
scale-free networks , and for fractal scale-free networks , where is the dimension of the fractal network. We support
these results by explicit calculations on four real networks: pharmaceutical
firms (N=6776), yeast (N=1458), WWW (N=2526), and a sample of Internet network
at AS level (N=20566), where is the number of nodes in the largest
connected component of a network. We also study the crossover phenomenon from
fractal to non-fractal networks upon adding random edges to a fractal network.
We show that the crossover length , separating fractal and
non-fractal regimes, scales with dimension of the network as
, where is the density of random edges added to the network.
We find that the correlation between degree and betweenness centrality
increases with .Comment: 19 pages, 6 figures. Submitted to PR
Structure of Business Firm Networks and Scale-Free Models.
We study the structure of business firm networks in the Life Sciences (LS) and the Information and Communication Technology (ICT) sectors. We analyze business firm networks and scale-free models with degree distribution P(q) proportional to (q + c)^-λ using the method of k-shell decomposition. We find that the LS network consists of three components: a "nucleus", which is a small well connected subgraph, "tendrils", which are small subgraphs consisting of small degree nodes connected exclusively to the nucleus, and a "bulk body" which consists of the majority of nodes. At the same time we do not observe the above structure in the ICT network. Our results suggest that the sizes of the nucleus and the tendrils decrease as λ increases and disappear for λ greater or equal to 3. We compare the k-shell structure of random scale-free model networks with the real world business firm networks. The observed behavior of the k-shell structure in the two industries is consistent with a recently proposed growth model that assumes the coexistence of both preferential and random regimes in the evolution of industry networks.
Cosmological networks
Networks often represent systems that do not have a long history of study in traditional fields of physics; albeit, there are some notable exceptions, such as energy landscapes and quantum gravity. Here, we consider networks that naturally arise in cosmology. Nodes in these networks are stationary observers uniformly distributed in an expanding open Friedmann-Lemaitre-Robertson-Walker universe with any scale factor and two observers are connected if one can causally influence the other. We show that these networks are growing Lorentz-invariant graphs with power-law distributions of node degrees. These networks encode maximum information about the observable universe available to a given observer
Diffuse Nontoxic Goiter in Children and Its Impact on Dental Pathology
The objective of the research was to assess the thyroid status of children with diffuse nontoxic goiter and its effect on dental pathology depending on age.Materials and methods. Clinical observation of 226 children at the age of 12-15 years was conducted. To analyze their thyroid status, serum levels of total thyroxine, free thyroxine, total triiodothyronine, and thyroid stimulating hormone were determined using enzyme immunoassay. The following thyroid indices were calculated for the integral estimation of the functional state of the pituitary-thyroid system: the peripheral inversion index (total triiodothyronine/total thyroxine), the integral index (total triiodothyronine + total thyroxine/thyroid stimulating hormone) and the indices of thyroid stimulating hormone/total triiodothyronine and thyroid stimulating hormone/total thyroxine. Their dental status was determined by means of standard indices recommended by the World Health Organization.Conclusions. In children with euthyroid enlargement of the thyroid gland, there were detected changes in the thyroid status within the reference range. According to the direction of changes in the most indices, dysthyroidism is characterized by the reduced thyroid function that can affect metabolic processes in the body, including the dentofacial system, as evidenced by significantly worse indices of the intensity of damage to hard dental tissues and periodontal tissues in children with diffuse nontoxic goiter
Hidden Variables in Bipartite Networks
We introduce and study random bipartite networks with hidden variables. Nodes
in these networks are characterized by hidden variables which control the
appearance of links between node pairs. We derive analytic expressions for the
degree distribution, degree correlations, the distribution of the number of
common neighbors, and the bipartite clustering coefficient in these networks.
We also establish the relationship between degrees of nodes in original
bipartite networks and in their unipartite projections. We further demonstrate
how hidden variable formalism can be applied to analyze topological properties
of networks in certain bipartite network models, and verify our analytical
results in numerical simulations
Betweenness Centrality of Fractal and Non-Fractal Scale-Free Model Networks and Tests on Real Networks
We study the betweenness centrality of fractal and non-fractal scale-free network models as well as real networks. We show that the correlation between degree and betweenness centrality C of nodes is much weaker in fractal network models compared to non-fractal models. We also show that nodes of both fractal and non-fractal scale-free networks have power law betweenness centrality distribution P(C) ~ C^ÎŽ. We find that for non-fractal scale-free networks ÎŽ = -2, and for fractal scale-free networks ÎŽ = -2 + 1/dB, where dB is the dimension of the fractal network. We support these results by explicit calculations on four real networks: pharmaceutical firms (N = 6776), yeast (N = 1458), WWW (N = 2526), and a sample of Internet network at AS level (N = 20566), where N is the number of nodes in the largest connected component of a network. We also study the crossover phenomenon from fractal to non-fractal networks upon adding random edges to a fractal network. We show that the crossover length â*, separating fractal and non-fractal regimes, scales with dimension dB of the network as pâ1/dB, where p is the density of random edges added to the network. We find that the correlation between degree and betweenness centrality increases with p.Interfirm networks; R&D collaborations, Pharmaceutical industry; ICT.
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