Self-generated Self-similar Traffic

Self-similarity in the network traffic has been studied from several aspects: both at the user side and at the network side there are many sources of the long range dependence. Recently some dynamical origins are also identified: the TCP adaptive congestion avoidance algorithm itself can produce chaotic and long range dependent throughput behavior, if the loss rate is very high. In this paper we show that there is a close connection between the static and dynamic origins of self-similarity: parallel TCPs can generate the self-similarity themselves, they can introduce heavily fluctuations into the background traffic and produce high effective loss rate causing a long range dependent TCP flow, however, the dropped packet ratio is low.Comment: 8 pages, 12 Postscript figures, accepted in Nonlinear Phenomena in Complex System

Directed network modules

A search technique locating network modules, i.e., internally densely connected groups of nodes in directed networks is introduced by extending the Clique Percolation Method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Renyi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own webpages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and among Google's webpages the overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory networks tend to overlap via out-hubs.Comment: 21 pages, 10 figures, version 2: added two paragaph

Universal hierarchical behavior of citation networks

Many of the essential features of the evolution of scientific research are imprinted in the structure of citation networks. Connections in these networks imply information about the transfer of knowledge among papers, or, in other words, edges describe the impact of papers on other publications. This inherent meaning of the edges implies that citation networks can exhibit hierarchical features that are typical of networks based on decision making. In this paper, we investigate the hierarchical structure of citation networks consisting of papers in the same field. We find that the majority of the networks follow a universal trend towards a highly hierarchical state, and the various fields display differences only concerning (i)their phase in life (distance from the 'birth' of a field) or (ii)the characteristic time according to which they are approaching the stationary state. We also show by a simple argument that the alterations in the behavior are related to and can be understood by the degree of specialization corresponding to the fields. Our results suggest that during the accumulation of knowledge in a given field, some papers are gradually becoming relatively more influential than most other papers. © 2014 IOP Publishing Ltd and SISSA Medialab srl

Anomalous diffusion in the citation time series of scientific publications

We analyze the citation time-series of manuscripts in three different fields of science; physics, social science and technology. The evolution of the time-series of the yearly number of citations, namely the citation trajectories, diffuse anomalously, their variance scales with time proportional to t (2H ), where H not equal 1/2. We provide detailed analysis of the various factors that lead to the anomalous behavior: non-stationarity, long-ranged correlations and a fat-tailed increment distribution. The papers exhibit a high degree of heterogeneity across the various fields, as the statistics of the highest cited papers is fundamentally different from that of the lower ones. The citation data is shown to be highly correlated and non-stationary; as all the papers except the small percentage of them with high number of citations, die out in time

Preferential attachment of communities: the same principle, but a higher level

The graph of communities is a network emerging above the level of individual nodes in the hierarchical organisation of a complex system. In this graph the nodes correspond to communities (highly interconnected subgraphs, also called modules or clusters), and the links refer to members shared by two communities. Our analysis indicates that the development of this modular structure is driven by preferential attachment, in complete analogy with the growth of the underlying network of nodes. We study how the links between communities are born in a growing co-authorship network, and introduce a simple model for the dynamics of overlapping communities.Comment: 7 pages, 3 figure

A New Method for Computing Topological Pressure

The topological pressure introduced by Ruelle and similar quantities describe dynamical multifractal properties of dynamical systems. These are important characteristics of mesoscopic systems in the classical regime. Original definition of these quantities are based on the symbolic description of the dynamics. It is hard or impossible to find symbolic description and generating partition to a general dynamical system, therefore these quantities are often not accessible for further studies. Here we present a new method by which the symbolic description can be omitted. We apply the method for a mixing and an intermittent system.Comment: 8 pages LaTeX with revtex.sty, the 4 postscript figures are included using psfig.tex to appear in PR

Evaluation of vaccine-induced maternal immunity against classical swine fever

The vaccine-induced maternal immunity against classical swine fever (CSF) was investigated in this study. Eight sows were vaccinated with the Chinese strain of classical swine fever virus (CSFV). The length of time between vaccination and farrowing was 167-217 days. Milk samples from the front, middle and back udder sections and blood samples were taken from the sows on days 3 and 14 after farrowing. Blood samples were obtained from the piglets at the age of 3, 6 and 10 weeks. The antibody level of the milk was examined by ELISA, and that of blood samples by the virus neutralization (VN) test as well. In all 3-week-old piglets and in 80% of the 6-week-old animals the neutralizing antibody level reached the titre of 1:40. In none of the 10-week-old piglets did the titre exceed the value of 1:20, but in about 25% of the piglets it reached 1:20; the half of these piglets came from two litters. In none of the piglets did the antibody level reach the negative threshold in the ELISA test during the study. No significant differences were found between the udder sections in milk antibody level by ELISA