ASU Scholarship Showcase

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

In spite of well-documented health benefits of vegetarian diets, less is known regarding the effects of these diets on athletic performance. In this cross-sectional study, we compared elite vegetarian and omnivore adult endurance athletes for maximal oxygen uptake (VO2 max) and strength. Twenty-seven vegetarian (VEG) and 43 omnivore (OMN) athletes were evaluated using VO2 max testing on the treadmill, and strength assessment using a dynamometer to determine peak torque for leg extensions. Dietary data were assessed using detailed seven-day food logs. Although total protein intake was lower among vegetarians in comparison to omnivores, protein intake as a function of body mass did not differ by group (1.2 ± 0.3 and 1.4 ± 0.5 g/kg body mass for VEG and OMN respectively, p = 0.220). VO2 max differed for females by diet group (53.0 ± 6.9 and 47.1 ± 8.6 mL/kg/min for VEG and OMN respectively, p < 0.05) but not for males (62.6 ± 15.4 and 55.7 ± 8.4 mL/kg/min respectively). Peak torque did not differ significantly between diet groups. Results from this study indicate that vegetarian endurance athletes’ cardiorespiratory fitness was greater than that for their omnivorous counterparts, but that peak torque did not differ between diet groups. These data suggest that vegetarian diets do not compromise performance outcomes and may facilitate aerobic capacity in athletes.

The termites evolved eusociality and complex societies before the ants, but have been studied much less. The recent publication of the first two termite genomes provides a unique comparative opportunity, particularly because the sequenced termites represent opposite ends of the social complexity spectrum. Zootermopsis nevadensis has simple colonies with totipotent workers that can develop into all castes (dispersing reproductives, nest-inheriting replacement reproductives, and soldiers). In contrast, the fungus-growing termite Macrotermes natalensis belongs to the higher termites and has very large and complex societies with morphologically distinct castes that are life-time sterile. Here we compare key characteristics of genomic architecture, focusing on genes involved in communication, immune defenses, mating biology and symbiosis that were likely important in termite social evolution. We discuss these in relation to what is known about these genes in the ants and outline hypothesis for further testing.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

Eusocial insects, mostly Hymenoptera, have evolved unique colonial lifestyles that rely on the perception of social context mainly through pheromones, and chemoreceptors are hypothesized to have played important adaptive roles in the evolution of sociality. However, because chemoreceptor repertoires have been characterized in few social insects and their solitary relatives, a comprehensive examination of this hypothesis has not been possible. Here, we annotate ∼3,000 odorant and gustatory receptors in recently sequenced Hymenoptera genomes and systematically compare >4,000 chemoreceptors from 13 hymenopterans, representing one solitary lineage (wasps) and three independently evolved eusocial lineages (ants and two bees). We observe a strong general tendency for chemoreceptors to expand in Hymenoptera, whereas the specifics of gene gains/losses are highly diverse between lineages. We also find more frequent positive selection on chemoreceptors in a facultative eusocial bee and in the common ancestor of ants compared with solitary wasps. Our results suggest that the frequent expansions of chemoreceptors have facilitated the transition to eusociality. Divergent expression patterns of odorant receptors between honeybee and ants further indicate differential roles of chemoreceptors in parallel trajectories of social evolution.

Epigenetic inheritance plays an important role in mediating alternative phenotype in highly social species. In order to gain a greater understanding of epigenetic effects in societies, we investigated DNA methylation in the termite Zootermopsis nevadensis. Termites are the most ancient social insects, and developmentally distinct from highly-studied, hymenopteran social insects. We used replicated bisulfite-sequencing to investigate patterns of DNA methylation in both sexes and among castes of Z. nevadensis. We discovered that Z. nevadensis displayed some of the highest levels of DNA methylation found in insects. We also found strong differences in methylation between castes. Methylated genes tended to be uniformly and highly expressed demonstrating the antiquity of associations between intragenic methylation and gene expression. Differentially methylated genes were more likely to be alternatively spliced than not differentially methylated genes, and possessed considerable enrichment for development-associated functions. We further observed strong overrepresentation of multiple transcription factor binding sites and miRNA profiles associated with differential methylation, providing new insights into the possible function of DNA methylation. Overall, our results show that DNA methylation is widespread and associated with caste differences in termites. More generally, this study provides insights into the function of DNA methylation and the success of insect societies.

Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.