Role Of Network Structure Research Articles

Abiotic factors shape temporal variation in the structure of an ant–plant network

Despite recognition of key biotic processes in shaping the structure of biological communities, few empirical studies have explored the influences of abiotic factors on the structural properties of mutualistic networks. We tested whether temperature and precipitation contribute to temporal variation in the nestedness of mutualistic ant–plant networks. While maintaining their nested structure, nestedness increased with mean monthly precipitation and, particularly, with monthly temperature. Moreover, some species changed their role in network structure, shifting from peripheral to core species within the nested network. We could summarize that abiotic factors affect plant species in the vegetation (e.g., phenology), meaning presence/absence of food sources, consequently an increase/decrease of associations with ants, and finally, these variations to fluctuations in nestedness. While biotic factors are certainly important, greater attention needs to be given to abiotic factors as underlying determinants of the structures of ecological networks.

How network structure shapes pairwise correlations between integrate-and-fire neurons

Correlations between neurons can be caused by various mechanisms and can be pronounced on different levels – on the level of spikes or membrane potentials – and both in time and space. Here, we analyze quantitatively the role of network structure on pairwise correlations in a model of local recurrent networks of excitatory and inhibitory leaky integrate-and-fire neurons. Especially correlations between the input currents of neurons prove to be highly sensitive to the statistical features of network structure like topology and weight distribution. We studied a variety of network topologies, ranging from random to small-world and regular [1,2]. If the external input spike trains are uncorrelated and Poissonian, input correlations between neurons are predominantly due to direct and indirect common input that is inherent to the recurrence of the local network. We speak of direct common input if two neurons share a common presynaptic neuron, and of indirect common input if two neurons receive inputs from two different presynaptic neurons, which in turn share a common presynaptic neuron.

Role of network structure and network effects in diffusion of innovations

Why does diffusion of innovation sometimes propagate throughout the whole population, and why at other times does it halt in its interim process? The current paper provides a potential answer to this question by developing a simple computational model of social networks. The proposed computational approach incorporating small-world graphs enables the authors to find that diffusion of innovation is more likely to fail in a random network than in a highly clustered network of consumers. A marketing implication is that the choice of initial target groups and their network structures matter in influencing whether an innovation makes full or partial penetration, in markets where network effects plays a role.

Symmetry of Metabolic Network

Previous studies of properties of metabolic works have mainly focused on the statistic properties of networks, including the small world, and power-law distribution of node degree, and building block of network motifs. Symmetry in the metabolic networks has not been systematically investigated. In this report, symmetry in directed graph was introduced and an algorithm to calculate symmetry in directed and disconnected graphs was developed. We calculated several indices to measure the degree of symmetry and compared them with random networks. We showed that metabolic networks in KEGG and BioCyc databases are generally symmetric and in particular locally symmetric. We found that symmetry in metabolic networks is distinctly higher than that in random networks. We obtained all the orbits in networks which are defined as structurally equivalent nodes and found that compound pairs in the same orbit show much more similarity in chemical structures and function than random compound pairs in network, which suggests that symmetry in the metabolic network can generate the functional redundancy, increase the robustness and play an important role in network structure, function and evolution.

Technology: the role of network structures

Technology: the role of network structures

An exploratory study on the roles of network structure and knowledge processing orientation in work unit knowledge management

Knowledge management (KM), which concerns itself with the creation, storage, access and dissemination of intellectual assets, has been a dominant research theme in recent years. However, given the fact that organizational units differ in the way they process their knowledge, there is a need to understand how informational benefits that a unit within the organization accrues by virtue of its relationships with others in the network impact its creativity and KM effectiveness. In this study, we explore the potential contribution of network characteristics to both the creativity and overall KM effectiveness of intra-organizational units. In attempting to reaffirm and extend previous study findings, we accomplish our initial research objective by relating network characteristics to innovative outcomes with a more complete set of network centrality measures. In addition, the study demonstrates that when there is a fit between network structure and knowledge processing orientation, the effectiveness of perceived KM in the units will improve. Our results suggest that fostering an organizational climate that is conducive to extensive interaction and communication between units is likely to make the units more innovative throughout the organization

Likelihood-based inference for stochastic models of sexual network formation

Sexually-transmitted diseases (STDs) constitute a major public health concern. Mathematical models for the transmission dynamics of STDs indicate that heterogeneity in sexual activity level allow them to persist even when the typical behavior of the population would not support endemicity. This insight focuses attention on the distribution of sexual activity level in a population. In this paper, we develop several stochastic process models for the formation of sexual partnership networks. Using likelihood-based model selection procedures, we assess the fit of the different models to three large distributions of sexual partner counts: (1) Rakai, Uganda, (2) Sweden, and (3) the USA. Five of the six single-sex networks were fit best by the negative binomial model. The American women's network was best fit by a power-law model, the Yule. For most networks, several competing models fit approximately equally well. These results suggest three conclusions: (1) no single unitary process clearly underlies the formation of these sexual networks, (2) behavioral heterogeneity plays an essential role in network structure, (3) substantial model uncertainty exists for sexual network degree distributions. Behavioral research focused on the mechanisms of partnership formation will play an essential role in specifying the best model for empirical degree distributions. We discuss the limitations of inferences from such data, and the utility of degree-based epidemiological models more generally.

Swelling, elasticity and transport properties of conjugated covalent electronic conducting gels : role of network structure.

We prepared fully conjugated covalent polymer gels of poly(octylthiophene). They are rubbery, luminescent and electronic conducting. These gels swell in organic solvents both in undoped and doped states. The active network chain segments which give rise both to elastic and swelling properties in undoped state are the linear strands comprised between adjacent cross-links. In swollen doped state, gels remain rubbery. In doped state, variation of conductivity as a function of the swelling ratio Q shows three zones which result from the relative predominance of intra to inter-chain hopping mechanisms.