38,349 research outputs found
Solar activity prediction of sunspot numbers (verification). Predicted solar radio flux; predicted geomagnetic indices Ap and Kp
Efforts to further verify a previously reported technique for predicting monthly sunspot numbers over a period of years (1979 to 1989) involved the application of the technique over the period for the maximum epoch of solar cycle 19. Results obtained are presented. Methods and results for predicting solar flux (F10.7 cm) based on flux/sunspot number models, ascent and descent, and geomagnetic activity indices as a function of sunspot number and solar cycle phase classes are included
Shuttle program. Solar activity prediction of sunspot numbers, predicted solar radio flux
A solar activity prediction technique for monthly mean sunspot numbers over a period of approximately ten years from February 1979 to January 1989 is presented. This includes the predicted maximum epoch of solar cycle 21, approximately January 1980, and the predicted minimum epoch of solar cycle 22, approximately March 1987. Additionally, the solar radio flux 10.7 centimeter smooth values are included for the same time frame using a smooth 13 month empirical relationship. The incentive for predicting solar activity values is the requirement of solar flux data as input to upper atmosphere density models utilized in mission planning satellite orbital lifetime studies
Detecting rich-club ordering in complex networks
Uncovering the hidden regularities and organizational principles of networks
arising in physical systems ranging from the molecular level to the scale of
large communication infrastructures is the key issue for the understanding of
their fabric and dynamical properties [1-5]. The ``rich-club'' phenomenon
refers to the tendency of nodes with high centrality, the dominant elements of
the system, to form tightly interconnected communities and it is one of the
crucial properties accounting for the formation of dominant communities in both
computer and social sciences [4-8]. Here we provide the analytical expression
and the correct null models which allow for a quantitative discussion of the
rich-club phenomenon. The presented analysis enables the measurement of the
rich-club ordering and its relation with the function and dynamics of networks
in examples drawn from the biological, social and technological domains.Comment: 1 table, 3 figure
Characterizing the structure of small-world networks
We give exact relations which are valid for small-world networks (SWN's) with
a general `degree distribution', i.e the distribution of nearest-neighbor
connections. For the original SWN model, we illustrate how these exact
relations can be used to obtain approximations for the corresponding basic
probability distribution. In the limit of large system sizes and small
disorder, we use numerical studies to obtain a functional fit for this
distribution. Finally, we obtain the scaling properties for the mean-square
displacement of a random walker, which are determined by the scaling behavior
of the underlying SWN
A dual modelling of evolving political opinion networks
We present the result of a dual modeling of opinion network. The model
complements the agent-based opinion models by attaching to the social agent
(voters) network a political opinion (party) network having its own intrinsic
mechanisms of evolution. These two sub-networks form a global network which can
be either isolated from or dependent on the external influence. Basically, the
evolution of the agent network includes link adding and deleting, the opinion
changes influenced by social validation, the political climate, the
attractivity of the parties and the interaction between them. The opinion
network is initially composed of numerous nodes representing opinions or
parties which are located on a one dimensional axis according to their
political positions. The mechanism of evolution includes union, splitting,
change of position and of attractivity, taken into account the pairwise node
interaction decaying with node distance in power law. The global evolution ends
in a stable distribution of the social agents over a quasi-stable and
fluctuating stationary number of remaining parties. Empirical study on the
lifetime distribution of numerous parties and vote results is carried out to
verify numerical results
Potts Model On Random Trees
We study the Potts model on locally tree-like random graphs of arbitrary
degree distribution. Using a population dynamics algorithm we numerically solve
the problem exactly. We confirm our results with simulations. Comparisons with
a previous approach are made, showing where its assumption of uniform local
fields breaks down for networks with nodes of low degree.Comment: 10 pages, 3 figure
Graph Metrics for Temporal Networks
Temporal networks, i.e., networks in which the interactions among a set of
elementary units change over time, can be modelled in terms of time-varying
graphs, which are time-ordered sequences of graphs over a set of nodes. In such
graphs, the concepts of node adjacency and reachability crucially depend on the
exact temporal ordering of the links. Consequently, all the concepts and
metrics proposed and used for the characterisation of static complex networks
have to be redefined or appropriately extended to time-varying graphs, in order
to take into account the effects of time ordering on causality. In this chapter
we discuss how to represent temporal networks and we review the definitions of
walks, paths, connectedness and connected components valid for graphs in which
the links fluctuate over time. We then focus on temporal node-node distance,
and we discuss how to characterise link persistence and the temporal
small-world behaviour in this class of networks. Finally, we discuss the
extension of classic centrality measures, including closeness, betweenness and
spectral centrality, to the case of time-varying graphs, and we review the work
on temporal motifs analysis and the definition of modularity for temporal
graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and
Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201
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