Centrality anomalies for the domestic air transportation networks in the USA: an empirical benchmark.

Air transportation systems are a foundational infrastructure for the human's society. The lack of systematic and detailed investigation on a large amount of records for air flights has blocked seriously the deep understanding of the systems. By using the American domestic passenger flight records from 1995 to 2020, we constructed the air transportation networks and calculated the betweenness and the eigenvector centralities for the airports. It is found that in terms of eigenvector centrality, 15-30% airports in the unweighted and undirected networks behave anomalous. The anomalies disappear after considering the information of link weights or directionalites. Five widely used models for air transportation networks are evaluated, results for which tell us that the spatial constraints are required to eliminate the anomalies detected by the eigenvector centrality, and provide us some references for selecting the parameters in the models. We hope the empirical benchmarks reported in this paper can stimulate much more works on theoretical models for air transportation systems.

Phase transition in a coevolving network of conformist and contrarian voters.

In the coevolving voter model, each voter has one of two diametrically opposite opinions, and a voter encountering a neighbor with the opposite opinion may either adopt it or rewire the connection to another randomly chosen voter sharing the same opinion. As we smoothly change the relative frequency of rewiring compared to that of adoption, there occurs a phase transition between an active phase and a frozen phase. By performing extensive Monte Carlo calculations, we show that the phase transition is characterized by critical exponents ß=0.54(1) and ν[over ¯]=1.5(1), which differ from the existing mean-field-type prediction. We furthermore extend the model by introducing a contrarian type that tries to have neighbors with the opposite opinion, and show that the critical behavior still belongs to the same universality class irrespective of such contrarians' fraction.

[The pathogenesis of CD4(+)T cells infiltrated into the spinal cord in rat SNL model].

AIM: To explore the infiltration pathogenesis of CD4(+);T cells following the spinal nerve ligation. METHODS: Healthy adult male SD rats were randomly divided into the spinal nerve ligation group (Tx), sham operation group (S), control group (C). the 50& mechanical paw withdrawal threshold ( 50&MWT ) was determined by up-down method; CD4(+);T cells infiltration was assessed by FACS; the mRNA levels of CCL2, CCL5 and CXCL10 were quantitated by RT-qPCR; serum cytokines were tested by ELISA kits. RESULTS: After 3 days since operation, 50&MWT of Tx group was significantly reduced (P<0.01) comparing with S group, C group; on day 14, 50&MWT was up to the minimum value; whereas S group and C group were no difference (P>0.05). After 7 days since operation, CD4(+);T cells infiltration into lumbar segments of the spinal cord in the Tx group increased significantly (P<0.01), and the CCL2, CCL5mRNA expression increased (P<0.05); on day 14, the CD4(+);T cells infiltration in Tx group was higher than S group, C group; but there was no statistical significance. On day 7 and 14 days, serum levels of cytokines were no difference in the three groups. CONCLUSION: Following spinal nerve ligation, high expression of chemokine promoted peripheral CD4(+);T cells to infiltrate into spinal cord; and the infiltrated CD4(+);T cells maintained the neuropathic pain.

Scaling of critical connectivity of mobile ad hoc networks.

In this paper, critical global connectivity of mobile ad hoc networks (MANETs) is investigated. We model the two-dimensional plane on which nodes move randomly with a triangular lattice. Demanding the best communication of the network, we account the global connectivity eta as a function of occupancy sigma of sites in the lattice by mobile nodes. Critical phenomena of the connectivity for different transmission ranges r are revealed by numerical simulations, and these results fit well to the analysis based on the assumption of homogeneous mixing. Scaling behavior of the connectivity is found as eta approximately f(R;{beta}sigma) , where R=(r-r_{0})r_{0} , r_{0} is the length unit of the triangular lattice, and beta is the scaling index in the universal function f(x) . The model serves as a sort of geometric distance-dependent site percolation on dynamic complex networks. Moreover, near each critical sigma_{c}(r) corresponding to certain transmission range r , there exists a cutoff degree k_{c} below which the clustering coefficient of such self-organized networks keeps a constant while the averaged nearest-neighbor degree exhibits a unique linear variation with the degree k , which may be useful to the designation of real MANETs.

Universal scaling behavior of clustering coefficient induced by deactivation mechanism.

We propose a model of network growth that generalizes the deactivation model previously suggested for complex networks. Several topological features of this generalized model, such as the degree distribution and clustering coefficient, have been investigated analytically and by simulations. A scaling behavior of clustering coefficient C approximately 1/M is theoretically obtained, where M refers to the number of active nodes in the network. We discuss the relationship between the recently observed numerical behavior of clustering coefficient in the coauthor and paper citation networks and our theoretical result. It shows that both of them are induced by deactivation mechanism. By introducing a perturbation, the generated network undergoes a transition from large to small world, meanwhile the scaling behavior of C is conserved. It indicates that C approximately 1/M is a universal scaling behavior induced by deactivation mechanism.

Scaling of directed dynamical small-world networks with random responses.

A dynamical model of small-world networks, with directed links which describe various correlations in social and natural phenomena, is presented. Random responses of sites to the input message are introduced to simulate real systems. The interplay of these ingredients results in the collective dynamical evolution of a spinlike variable S(t) of the whole network. The global average spreading length (s) and average spreading time (s) are found to scale as p(-alpha)ln(N with different exponents. Meanwhile, S(t) behaves in a duple scaling form for N>>N(*): S approximately f(p(-beta)q(gamma)t), where p and q are rewiring and external parameters, alpha, beta, and gamma are scaling exponents, and f(t) is a universal function. Possible applications of the model are discussed.