1,398 research outputs found
Nonlocal evolution of weighted scale-free networks
We introduce the notion of globally updating evolution for a class of
weighted networks, in which the weight of a link is characterized by the amount
of data packet transport flowing through it. By noting that the packet
transport over the network is determined nonlocally, this approach can explain
the generic nonlinear scaling between the strength and the degree of a node. We
demonstrate by a simple model that the strength-driven evolution scheme
recently introduced can be generalized to a nonlinear preferential attachment
rule, generating the power-law behaviors in degree and in strength
simultaneously.Comment: 4 pages, 4 figures, final version published in PR
The diplomat's dilemma: Maximal power for minimal effort in social networks
Closeness is a global measure of centrality in networks, and a proxy for how
influential actors are in social networks. In most network models, and many
empirical networks, closeness is strongly correlated with degree. However, in
social networks there is a cost of maintaining social ties. This leads to a
situation (that can occur in the professional social networks of executives,
lobbyists, diplomats and so on) where agents have the conflicting objectives of
aiming for centrality while simultaneously keeping the degree low. We
investigate this situation in an adaptive network-evolution model where agents
optimize their positions in the network following individual strategies, and
using only local information. The strategies are also optimized, based on the
success of the agent and its neighbors. We measure and describe the time
evolution of the network and the agents' strategies.Comment: Submitted to Adaptive Networks: Theory, Models and Applications, to
be published from Springe
The networked seceder model: Group formation in social and economic systems
The seceder model illustrates how the desire to be different than the average
can lead to formation of groups in a population. We turn the original, agent
based, seceder model into a model of network evolution. We find that the
structural characteristics our model closely matches empirical social networks.
Statistics for the dynamics of group formation are also given. Extensions of
the model to networks of companies are also discussed
Comparing Recent Organizing Templates for Test Content between ACS Exams in General Chemistry and AP Chemistry
Two different versions of âbig ideasâ rooted content maps have recently been published for general chemistry. As embodied in the content outline from the College Board, one of these maps is designed to guide curriculum development and testing for advanced placement (AP) chemistry. The Anchoring Concepts Content Map for general chemistry from the ACS Exams Institute is a component of a larger content map for the four-year undergraduate curriculum. This article compares the structure and content in these two maps to provide perspective on the current nature of the general chemistry curriculum. This contribution is part of a special issue on teaching introductory chemistry in the context of the AP chemistry course redesign
Cascade-based attacks on complex networks
We live in a modern world supported by large, complex networks. Examples
range from financial markets to communication and transportation systems. In
many realistic situations the flow of physical quantities in the network, as
characterized by the loads on nodes, is important. We show that for such
networks where loads can redistribute among the nodes, intentional attacks can
lead to a cascade of overload failures, which can in turn cause the entire or a
substantial part of the network to collapse. This is relevant for real-world
networks that possess a highly heterogeneous distribution of loads, such as the
Internet and power grids. We demonstrate that the heterogeneity of these
networks makes them particularly vulnerable to attacks in that a large-scale
cascade may be triggered by disabling a single key node. This brings obvious
concerns on the security of such systems.Comment: 4 pages, 4 figures, Revte
Edge overload breakdown in evolving networks
We investigate growing networks based on Barabasi and Albert's algorithm for
generating scale-free networks, but with edges sensitive to overload breakdown.
the load is defined through edge betweenness centrality. We focus on the
situation where the average number of connections per vertex is, as the number
of vertices, linearly increasing in time. After an initial stage of growth, the
network undergoes avalanching breakdowns to a fragmented state from which it
never recovers. This breakdown is much less violent if the growth is by random
rather than preferential attachment (as defines the Barabasi and Albert model).
We briefly discuss the case where the average number of connections per vertex
is constant. In this case no breakdown avalanches occur. Implications to the
growth of real-world communication networks are discussed.Comment: To appear in Phys. Rev.
Dynamic scaling regimes of collective decision making
We investigate a social system of agents faced with a binary choice. We
assume there is a correct, or beneficial, outcome of this choice. Furthermore,
we assume agents are influenced by others in making their decision, and that
the agents can obtain information that may guide them towards making a correct
decision. The dynamic model we propose is of nonequilibrium type, converging to
a final decision. We run it on random graphs and scale-free networks. On random
graphs, we find two distinct regions in terms of the "finalizing time" -- the
time until all agents have finalized their decisions. On scale-free networks on
the other hand, there does not seem to be any such distinct scaling regions
Multiscale Analysis of Spreading in a Large Communication Network
In temporal networks, both the topology of the underlying network and the
timings of interaction events can be crucial in determining how some dynamic
process mediated by the network unfolds. We have explored the limiting case of
the speed of spreading in the SI model, set up such that an event between an
infectious and susceptible individual always transmits the infection. The speed
of this process sets an upper bound for the speed of any dynamic process that
is mediated through the interaction events of the network. With the help of
temporal networks derived from large scale time-stamped data on mobile phone
calls, we extend earlier results that point out the slowing-down effects of
burstiness and temporal inhomogeneities. In such networks, links are not
permanently active, but dynamic processes are mediated by recurrent events
taking place on the links at specific points in time. We perform a multi-scale
analysis and pinpoint the importance of the timings of event sequences on
individual links, their correlations with neighboring sequences, and the
temporal pathways taken by the network-scale spreading process. This is
achieved by studying empirically and analytically different characteristic
relay times of links, relevant to the respective scales, and a set of temporal
reference models that allow for removing selected time-domain correlations one
by one
Attack Resilience of the Evolving Scientific Collaboration Network
Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local (âmicroscopicâ) level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level â fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks
Structural transitions in scale-free networks
Real growing networks like the WWW or personal connection based networks are
characterized by a high degree of clustering, in addition to the small-world
property and the absence of a characteristic scale. Appropriate modifications
of the (Barabasi-Albert) preferential attachment network growth capture all
these aspects. We present a scaling theory to describe the behavior of the
generalized models and the mean field rate equation for the problem. This is
solved for a specific case with the result C(k) ~ 1/k for the clustering of a
node of degree k. Numerical results agree with such a mean-field exponent which
also reproduces the clustering of many real networks.Comment: 4 pages, 3 figures, RevTex forma
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