Psychosocial and educational outcomes of weight faltering in infancy in ALSPAC

OBJECTIVES: To investigate whether infants with weight faltering have impaired psychosocial and educational outcomes in later childhood. DESIGN: Follow-up of infants with weight faltering in a large UK cohort study. SETTING: The Avon Longitudinal Study of Parents and Children (ALSPAC). PARTICIPANTS: 11 534 term infants from ALSPAC with complete weight records. Weight gain (conditional on initial weight) was calculated for three periods: from birth to 8 weeks, 8 weeks to 9 months, and birth to 9 months. Cases of weight faltering were defined as those infants with a conditional weight gain below the 5th centile, and these were compared with the rest of the cohort as the control group. OUTCOMES: Between 6 and 11 years, social, emotional and behavioural development was measured by direct assessment of the children and parental and teacher report. Educational outcomes included Standardised Assessment Test results at 7 and 11 years and Special Educational Needs status at age 11. RESULTS: Differences seen on univariate analysis in attention, non-verbal accuracy, educational attainment and special educational needs became non-significant after adjustment for confounding. Children with weight faltering in infancy did not differ from controls on any measures of self-esteem, peer relationships, experience of bullying, social cognition, antisocial activities, anxiety, depression or behavioural problems. CONCLUSIONS: Weight faltering in early infancy was associated with poorer educational outcomes in later childhood, but these associations were explained by confounding. The subsequent psychosocial development of infants with slow weight gain was not different from that of their peers

Cascade control and defense in complex networks

Complex networks with heterogeneous distribution of loads may undergo a global cascade of overload failures when highly loaded nodes or edges are removed due to attacks or failures. Since a small attack or failure has the potential to trigger a global cascade, a fundamental question regards the possible strategies of defense to prevent the cascade from propagating through the entire network. Here we introduce and investigate a costless strategy of defense based on a selective further removal of nodes and edges, right after the initial attack or failure. This intentional removal of network elements is shown to drastically reduce the size of the cascade.Comment: 4 pages, 2 figures, Revte

Role-similarity based functional prediction in networked systems: Application to the yeast proteome

We propose a general method to predict functions of vertices where: 1. The wiring of the network is somehow related to the vertex functionality. 2. A fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme is tested on model networks were the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods

Core-periphery organization of complex networks

Networks may, or may not, be wired to have a core that is both itself densely connected and central in terms of graph distance. In this study we propose a coefficient to measure if the network has such a clear-cut core-periphery dichotomy. We measure this coefficient for a number of real-world and model networks and find that different classes of networks have their characteristic values. For example do geographical networks have a strong core-periphery structure, while the core-periphery structure of social networks (despite their positive degree-degree correlations) is rather weak. We proceed to study radial statistics of the core, i.e. properties of the n-neighborhoods of the core vertices for increasing n. We find that almost all networks have unexpectedly many edges within n-neighborhoods at a certain distance from the core suggesting an effective radius for non-trivial network processes

Majority-vote model on hyperbolic lattices

We study the critical properties of a non-equilibrium statistical model, the majority-vote model, on heptagonal and dual heptagonal lattices. Such lattices have the special feature that they only can be embedded in negatively curved surfaces. We find, by using Monte Carlo simulations and finite-size analysis, that the critical exponents $1/\nu$, $\beta/\nu$ and $\gamma/\nu$ are different from those of the majority-vote model on regular lattices with periodic boundary condition, which belongs to the same universality class as the equilibrium Ising model. The exponents are also from those of the Ising model on a hyperbolic lattice. We argue that the disagreement is caused by the effective dimensionality of the hyperbolic lattices. By comparative studies, we find that the critical exponents of the majority-vote model on hyperbolic lattices satisfy the hyperscaling relation $2\beta/\nu+\gamma/\nu=D_{\mathrm{eff}}$, where $D_{\mathrm{eff}}$ is an effective dimension of the lattice. We also investigate the effect of boundary nodes on the ordering process of the model.Comment: 8 pages, 9 figure

Modularity and community structure in networks

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.Comment: 7 pages, 3 figure

Network dynamics of ongoing social relationships

Many recent large-scale studies of interaction networks have focused on networks of accumulated contacts. In this paper we explore social networks of ongoing relationships with an emphasis on dynamical aspects. We find a distribution of response times (times between consecutive contacts of different direction between two actors) that has a power-law shape over a large range. We also argue that the distribution of relationship duration (the time between the first and last contacts between actors) is exponentially decaying. Methods to reanalyze the data to compensate for the finite sampling time are proposed. We find that the degree distribution for networks of ongoing contacts fits better to a power-law than the degree distribution of the network of accumulated contacts do. We see that the clustering and assortative mixing coefficients are of the same order for networks of ongoing and accumulated contacts, and that the structural fluctuations of the former are rather large.Comment: to appear in Europhys. Let