The transsortative structure of networks.

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.

Research on growth of innovations introduced to the market has gradually shifted its focus from aggregate-level diffusion to exploring how growth is influenced by a given social network structure's characteristics. In this paper, we critically review this branch of literature. We argue that the growth of an innovation in a social network is shaped by the network's structure. Borrowing from the field of industrial organization in economics, which defines itself as the study of the effect of market structure on market performance, we describe this new wave of research on growth of innovations as the effect of social network structure on innovation performance. Hence, social network structural characteristics should be incorporated into research on new product growth as well as into managerial marketing decisions such as targeting and new product seeding.We review how social network structure influences innovations' market performance. Specifically, we discuss (1) a networks' global characteristics, namely average degree, degree distribution, clustering, and degree assortativity; (2) dyadic characteristics, or the relationships between pairs of network members, namely tie strength and embeddedness; (3) intrinsic individual characteristics, namely opinion leadership and susceptibility; and (4) location-based individual characteristics, namely the degree centrality, closeness centrality, and betweenness centrality of an individual network member.Overall, we find that growth is particularly effective in networks that demonstrate the “3 Cs”: cohesion (strong mutual influence among its members), connectedness (high number of ties), and conciseness (low redundancy). We identify gaps in current knowledge, discuss the implications on managerial decision making, and suggest topics for future research.

Networks of citations among the papers on a research topic reflect the structure of scholarship on that topic. Reference-network data for research areas from several disciplines show substantial variation in the structure of scholarship, ranging from the frequent and disproportionate citation of recent work to the frequent and disproportionate citation of foundational documents. The variation is inconsistent with the pattern expected of a simple physical sciences-behavioral sciences-humanities dimension. Citation-context analyses of references in the networks of various fields suggest that variation in network structure is due in part to differences in why authors cite their colleagues' work: Disproportionate citation of foundational documents occurs when authors cite papers as examples of perspectives or general approaches rather than as support for specific points. Differences in use patterns for citations can help us understand other differences among scholarly communities

The topological (graph) structure of complex networks often provides valuable information about the performance and vulnerability of the network. However, there are multiple ways to represent a given network as a graph. Electric power transmission and distribution networks have a topological structure that is straightforward to represent and analyze as a graph. However, simple graph models neglect the comprehensive connections between components that result from Ohm's and Kirchhoff's laws. This paper describes the structure of the three North American electric power interconnections, from the perspective of both topological and electrical connectivity. We compare the simple topology of these networks with that of random (Erdos and Renyi, 1959), preferential-attachment (Barabasi and Albert, 1999) and small-world (Watts and Strogatz, 1998) networks of equivalent sizes and find that power grids differ substantially from these abstract models in degree distribution, clustering, diameter and assortativity, and thus conclude that these topological forms may be misleading as models of power systems. To study the electrical connectivity of power systems, we propose a new method for representing electrical structure using electrical distances rather than geographic connections. Comparisons of these two representations of the North American power networks reveal notable differences between the electrical and topological structure of electric power networks.

The structural characteristics of four epoxy adhesives, obtained by crosslinking an epoxy novolac with various levels of a substituted imidazole curing agent, were investigated and correlated with thermal and mechanical properties. Variations in network structure were characterized by measuring crosslink densities and by qualitatively assessing glassy state free volume from densities and coefficients of thermal expansion. Differential scanning calorimetry was used to obtain glass transition temperatures, and dynamic mechanical thermal analysis was used to follow primary (alpha) and secondary (beta) transitions. Bulk behavior was characterized by tensile modulus, strength, and toughness, together with compressive modulus and yield strength. The effect of sub‐Tg aging on compressive yield strength was investigated as well. As the level of imidazole increased, crosslink density, and hence network packing efficiency and free volume, decreased. For fully cured networks, both the glass and the alpha transition temperatures increased with crosslink density. Calculated activation enthalpies and entropies indicated significant degrees of network cooperativity in the alpha transitions, particularly for the more highly crosslinked systems. Beta transition temperatures, however, were found to be independent of crosslink density. Bulk properties generally showed a dependence both on crosslink density and free volume. Yield stress, for example, was highest for the network with lowest crosslink density and free volume. Volume relaxation associated with physical aging also caused yield stress to increase.

Insects provide key pollination services in most terrestrial biomes, but this service depends on a multistep interaction between insect and plant. An insect needs to visit a flower, receive pollen from the anthers, move to another conspecific flower, and finally deposit the pollen on a receptive stigma. Each of these steps may be affected by climate change, and focusing on only one of them (e.g., flower visitation) may miss important signals of change in service provision. In this study, we combine data on visitation, pollen transport, and single‐visit pollen deposition to estimate functional outcomes in the high Arctic plant‐pollinator network of Zackenberg, Northeast Greenland, a model system for global warming–associated impacts in pollination services. Over two decades of rapid climate warming, we sampled the network repeatedly: in 1996, 1997, 2010, 2011, and 2016. Although the flowering plant and insect communities and their interactions varied substantially between years, as expected based on highly variable Arctic weather, there was no detectable directional change in either the structure of flower‐visitor networks or estimated pollen deposition. For flower‐visitor networks compiled over a single week, species phenologies caused major within‐year variation in network structure despite consistency across years. Weekly networks for the middle of the flowering season emerged as especially important because most pollination service can be expected to be provided by these large, highly nested networks. Our findings suggest that pollination ecosystem service in the high Arctic is remarkably resilient. This resilience may reflect the plasticity of Arctic biota as an adaptation to extreme and unpredictable weather. However, most pollination service was contributed by relatively few fly taxa (Diptera: Spilogona sanctipauli and Drymeia segnis [Muscidae] and species of Rhamphomyia [Empididae]). If these key pollinators are negatively affected by climate change, network structure and the pollination service that depends on it would be seriously compromised.

Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks. ESNs have been shown to be effective in predicting complex time series while reducing training costs, thereby improving efficiency. Deep echo state networks, which extend the architecture of ESNs by stacking multiple reservoir layers, have also been proposed as an option to learn different features of the dynamics across the layers, but to date they have received less study. In this work, we analyzed the performance of deep ESNs for several variations in network structure, including a hybrid approach that integrated a knowledge-based model. We found that for specific network structures, deep ESNs could improve prediction accuracy over baseline ESNs with the same number of neurons by up to 65% for a chaotic data set derived from the Mackey-Glass model and 14% for an experimental data set of complex zebrafish cardiac voltage recordings. Similarly, deep hybrid ESNs could reduce error compared to same-size flat hybrid ESNs by up to 59% for the Mackey-Glass data set and 11% for the experimental data set. Our results also showed that the hybrid approach was especially beneficial for the experimental data set and that deep network structures improved prediction robustness.

The structure of ecological networks, in particular food webs, determines their ability to evolve further, i.e. evolvability. The knowledge about how food web evolvability is determined by the structures of diverse ecological networks can guide human interventions purposefully to either promote or limit evolvability of ecosystems. However, the focus of prior food web studies was on stability and robustness; little is known regarding the impact of ecological network structures on their evolvability. To correlate ecosystem structure and evolvability, we adopt the NK model originally from evolutionary biology to generate and assess the ruggedness of fitness landscapes of a wide spectrum of model food webs with gradual variation in the amount of feeding loops and link density. The variation in network structures is controlled by linkage rewiring. Our results show that more feeding loops and lower trophic link density, i.e. higher autonomy of species, of food webs increase the potential for the ecosystem to generate heritable variations with improved fitness. Our findings allow the prediction of the evolvability of actual food webs according to their network structures, and provide guidance to enhancing or controlling the evolvability of specific ecosystems.

Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research.

People with mental health or substance abuse problems often face social difficulties. At the same time, research highlights the importance of social factors as determinants of mental health. These phenomena are distinct yet intertwined. Looking for explanations in health and social factors may seem complex but raises questions about linkages between actor and structure. In this study we aim to circumvent what we believe to be simplifications when understanding networks as “structural effects” by maintaining a grounded approach. We critically examine perceptions of networks in an attempt to bridge a gap between the individual and structural level by asking two questions: “What are the characteristics of the networks of users of community mental health services, and what aspects of their network do users highlight as important?”. The findings show some variation in network structures, but as expected, several participants reported small and/or homogeneous networks. A conscious attitude toward different but contradictory relational and personal perspectives seems to be significant. For example, maintaining a sufficient number of relationships conflicted with evaluations of one's own health and assessments of relationship qualities. We argue that this evokes a negotiating way of being and suggest that reflexivity might serve as a missing mechanism challenging structural perspectives yet simultaneously representing new structures.

Edge computing provides computing resources on the side close to the data source, which can reduce latency and bandwidth consumption. The growing distance between users and edge servers may lead to degradation of service quality or even service interruption. Attribute to the variation in network structures, different migration paths can generate different latency, and our work focuses on the selection of migration paths. In this paper, a grid-based path selection algorithm is proposed to perform heuristic path selection, which is based on the edge server load, transmission bandwidth, and physical location, and finally, obtain an optimal service migration path. Through real-data simulation experiments, we found that the algorithm proposed in this paper performs better in terms of running time, transmission time, and service following compared with other methods.

One of the most important and consistent findings in the social networks literature is that individuals are socially connected to people who resemble themselves. This finding extends to preferences for consumer culture. Whereas network theorists previously had presumed that social networks generated this outcome through a process of social influence, this traditional conception of the relationship between culture and social networks has been challenged by new theoretical perspectives and novel methodological techniques that enable researchers to empirically test the causal direction of the relationship. In this article, I first discuss how theorists have adjudicated between competing explanations of the relationship of taste preferences and social network structure. Second, I discuss how sociologists have theorized that tastes contribute to variation in network structure. Third, I examine survey and ethnographic research that discusses interactional mechanisms by which people actively mobilize culture to form social relationships. Fourth and finally, I discuss research that explores how consumer culture constitutes the meaning of social relationships and how relationship category affects theorizing of network formation.

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis-based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.

A series of ethylene propylene terpolymer vulcanizates, prepared by varying termonomer type, cure system, cure time and cure temperature, are characterized by determining the number and type of cross-links present. The termonomers used represent the types currently available in commercial quantities. Characterization is carried out by measuring the C1 constant of the Mooney Rivlin Saunders equation before and after treatment with the chemical probes propane-2-thiol/piperidine and n-hexane thiol/piperidine, thus making it possible to calculate the relative proportions of mono-sulphidic, di-sulphidic and poly- sulphidic cross-links. The cure systems used included both sulphur and peroxide formulations. Specific physical properties are determined for each network and an attempt is made to correlate observed changes in these with variations in network structure. A survey of the economics of each formulation based on a calculated efficiency parameter for each cure system is included. Values of C1 are calculated from compression modulus data after the reliability of the technique when used with ethylene propylene terpolymers had been established. This is carried out by comparing values from both compression and extension stress strain measurements for natural rubber vulcanizates and by assessing the effects of sample dimensions and the degree of swelling. The technique of compression modulus is much more widely applicable than previously thought. The basic structure of an ethylene propylene terpolymer network appears to be independent of the type of cure system used ( sulphur based systems only), the proportions of constituent cross-links being nearly constant.

An evolutionary perspective on economic geography requires a dynamic understanding of change in networks. This article explores theories of network evolution for their use in geography and develops the conceptual framework of geographical network trajectories. It specifically assesses how tie selection constitutes the evolutionary process of retention and variation in network structure and how geography affects these mechanisms. Finally, a typology of regional network formations is used to discuss opportunities for innovation in and across regions.