High Link Density Research Articles

Deciphering microeukaryotic–bacterial co-occurrence networks in coastal aquaculture ponds

Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems. Although their diversity and composition have been widely investigated in aquaculture systems, the co-occurrence bipartite network between microeukaryotes and bacteria remains poorly understood. This study used the bipartite network analysis of high-throughput sequencing datasets to detect the co-occurrence relationships between microeukaryotes and bacteria in water and sediment from coastal aquaculture ponds. Chlorophyta and fungi were dominant phyla in the microeukaryotic–bacterial bipartite networks in water and sediment, respectively. Chlorophyta also had overrepresented links with bacteria in water. Most microeukaryotes and bacteria were classified as generalists, and tended to have symmetric positive and negative links with bacteria in both water and sediment. However, some microeukaryotes with high density of links showed asymmetric links with bacteria in water. Modularity detection in the bipartite network indicated that four microeukaryotes and twelve uncultured bacteria might be potential keystone taxa among the module connections. Moreover, the microeukaryotic–bacterial bipartite network in sediment harbored significantly more nestedness than that in water. The loss of microeukaryotes and generalists will more likely lead to the collapse of positive co-occurrence relationships between microeukaryotes and bacteria in both water and sediment. This study unveils the topology, dominant taxa, keystone species, and robustness in the microeukaryotic–bacterial bipartite networks in coastal aquaculture ecosystems. These species herein can be applied for further management of ecological services, and such knowledge may also be very useful for the regulation of other eutrophic ecosystems.

Taking care of everyone’s business: interpreting Sicilian Mafia embedment through spatial network analysis

ABSTRACT Mafia-type organisations often have a strong geographical and cultural entrenchment in the territory they belong. However, their analysis as a spatially networked social structure is still missing. A combined socio-spatial network analysis is presented here, through the demise of a large police operation called Operazione Perseo in 2008. This approach is developed in two ways. At first, a visual representation of the social network of this large group of mafiosi embedded in a geographical space is presented. Three main salient territorial features of the network are thus highlighted. A high density of links in some neighbourhoods, as well as connections across different Mandamenti, the territorial units where Mafia families operate, and the correlation of links and socio-economic determinants, like the unemployment rates. Secondly, a spatial econometric analysis of centrality measures of the group is suggested here. Findings show a positive spatial correlation in the Eigenvalue centrality scores.

World mineral deposits in the table of periodic chemical elements

The distribution of mineral deposits and the distribution of chemical elements on the globe are characterized by heterogeneity. A wide range of publications of domestic and foreign specialists - geologists, geographers, geochemists, economists - were dedicated to mineral resources of the world, mineral deposits. During processing the material the comparative-geographical, cartographic (analysis of minerals maps, mineral resources in the context of continents and regions of the world, cartographic interpretation of Mendeleev periodical table), monographic (analysis of fundamental works of leading domestic and foreign geologists and resource scientists, geologists and geologists, and geologists and geologists) directories, multi-volume editions devoted to geology and mineral resources of individual countries and regions of the world) methods, systematic approach, and GIS technologies - all these were used for received data processing and systematization. Explored mineral deposits (current and potential) form on the planet both individual local deposits and geochemical zones – areas where economically valuable chemical elements and their compounds are concentrated, which are diverse in genesis, stocks, and possibilities of exploitation. The largest of the latter is the Appalachians in the US - the Western Hemisphere, the Highveld in South Africa, Khibiny and the Ural Mountains inRussia - the Eastern Hemisphere. The leading countries in which most geochemical resources are extracted from the subsoil are the United States (65% of the total elements of Mendeleev periodical table), Russia (48%), China (38%), Canada (38%), South Africa (30%), Australia, (27%), Kazakhstan (19%), India (14%), Mexico (13%). The ideas about the level of provision of mineral resources and minerals in individual countries and territories of the world were systematized. The Mendeleev periodical table and its mineral and raw content were presented as an objective factor in the international geographical distribution of labor. The illuminated issues are confirmed high density of interdisciplinary links (geology, geography, chemistry, geochemistry, ecology, economics, regional studies, zoning).

Design of Interactive Visualizations for Next-Generation Ultra-Large Communication Networks

With the increasing size and complexity of next-generation communication networks, it is critical to utilize interactive visualizations to support the monitoring, planning, and management of networks. Effectively visualizing large-scale networks is difficult with traditional methods because of the high link density and complex node relationships. Given the limited screen space, to assist Internet Service Provider’s (ISP) network planning and management activities, investigating how to present ultra-large-scale network data efficiently is crucial. This paper presents a real-time interactive visualization system that combines the design strategies of progressive disclosure and multiple panels to elegantly visualize the large-scale networks and avoid the information-overload problem. The system also visualizes the configuration of the network elements and provides the network performance information, including the port-level Quality of Service (QoS) metrics. Furthermore, the system enables navigation through the port-level connection and provides different modes for multiple purposes.

First network analysis of interspecific associations of abyssal benthic megafauna reveals potential vulnerability of abyssal hill community

The distribution of organisms is related to both environmental factors and interactions between organisms. However, such associations between organisms across an abyssal megafaunal community have not previously been investigated at landscape scale because of a lack of positional data on specimens over such scales. We quantified spatial distributions and investigated interspecific associations in benthic megafaunal communities in three contrasting habitats on the Porcupine Abyssal Plain, two on the abyssal plain and one on the flank of a modest abyssal hill (~50 m above the plain). We used a Bayesian Network Inference Algorithm approach, which considers the ecosystem as a network, facilitated by robust positioning of specimens determined through seabed photography captured with an autonomous underwater vehicle. We found non-random intraspecific distributions of most morphotypes in all areas. The organisms in two interspecific networks on the abyssal plain had high connectance and link density, while the network at the Hill site was notable in the lack of inter-dependencies and highly dependent on one morphotype – Ophiuroidea. The reduced connectance of the hill community suggests that it is operating under a different regime and potentially more vulnerable to perturbation than those on the plain. Interspecific dependencies on the abyssal plain occurred across broad taxonomic groupings, and were thought to be a result of similar relationships between pairs of organisms and the substrate, and competition for detrital resource. In addition, some intraspecific pairs changed dependency direction at different scales. Our results suggest that the scales of inter- and intraspecific aggregation will be important considerations in the design of community assessments, and in spatial planning for their conservation.

Enhanced performance of indoor VLC using anti-periodic asymmetrically clipped OFDM and multiple LEDS

Visible light communication (VLC) systems make use of the available LED lamps to realize a high spatial density of high-speed optical links superposed to their natural illumination function. Orthogonal frequency division multiplexing (OFDM) has been adopted for these intensity modulation/direct-detection optical channels. However, it suffers from low power efficiency or low spectral efficiency. In this paper, we aim to improve the performance of OFDM-based indoor VLC systems. By utilizing the principle of spatial summing, we propose an anti-periodic asymmetrically clipped OFDM scheme to make efficient use of both power and spectrum. In this scheme, the OFDM subcarriers are partitioned into multiple specific groups to form independent unipolar signals. These signals are simultaneously transmitted from different LEDs that are within the same light fixture and are summed in space before being detected by a conventional OFDM receiver. The computer simulation results show that the proposed approach can achieve lower PAPR and more robustness to the clipping distortion caused by the nonlinearities of LED leading to better bit error rate performance for the same spectral efficiency than that of the conventional DCO-OFDM and ACO-OFDM schemes do under both LOS and DOW channels.

Learning fuzzy cognitive maps with convergence using a multi-agent genetic algorithm

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. Recently, many evolutionary-based algorithms are proposed to learn FCMs from historical data by optimizing Data_Error, which is used to evaluate the difference between available response sequences and generated response sequences. However, when Data_Error is adopted as the fitness function for learning FCMs, two problems arise. One is that the optimization reaches the desired result slowly; the other is that the learned FCMs have high link density. To solve these problems, we propose another objective named as convergence error, which is inspired by the convergence of FCMs, to evaluate the difference between the convergent value of available response sequences and that of generated response sequences. In addition, a multi-agent genetic algorithm (MAGA), which is effective for large-scale global numerical optimization, is adopted to optimize convergence error for learning FCMs. To this end, a novel learning approach, a multi-agent genetic algorithm based on the convergence error (MAGA-Convergence), is proposed for learning FCMs. MAGA-Convergence needs less data, because the only initial value and convergent value of the available response sequences are needed for learning FCMs. In the experiments, MAGA-Convergence is applied to learn the FCMs of synthetic data and the benchmarks DREAM3 and DREAM4 for gene regulatory network reconstruction. The experimental results show that the learned FCMs are sparse and could be learned in much fewer generations than other learning algorithms.

The structure of plant spatial association networks is linked to plant diversity in global drylands.

1. Despite commonly used to unveil the complex structure of interactions within ecological communities and their value to assess their resilience against external disturbances, network analyses have seldom been applied in plant communities. We evaluated how plant-plant spatial association networks vary in global drylands, and assessed whether network structure was related to plant diversity in these ecosystems. 2. We surveyed 185 dryland ecosystems from all continents except Antarctica and built networks using the local spatial association between all the perennial plants species present in the communities studied. Then, for each network we calculated four descriptors of network structure (link density, link weight mean and heterogeneity, and structural balance), and evaluated their significance with null models. Finally, we used structural equation models to evaluate how abiotic factors (including geography, topography, climate and soil conditions) and network descriptors influenced plant species richness and evenness. 3. Plant networks were highly variable worldwide, but at most study sites (72%) presented common structures such as a higher link density than expected. We also find evidence of the presence of high structural balance in the networks studied. Moreover, all network descriptors considered had a positive and significant effect on plant diversity, and on species richness in particular. Synthesis. Our results constitute the first empirical evidence showing the existence of common network architectures structuring dryland plant communities at the global scale, and suggest a relationship between the structure of spatial networks and plant diversity. They also highlight the importance of system-level approaches to explain the diversity and structure of interactions in plant communities, two major drivers of terrestrial ecosystem functioning.

Numérotation des graphes sociaux basée sur les communautés pour la réduction des défauts de cache

Last version asked for publication 10th may; finally accepted in 6th April 2017; Accepted after minor changes in 17th October 2016, ABSTRACT. One of social graph's properties is the community structure, that is, subsets where nodes belonging to the same subset have a higher link density between themselves and a low link density with nodes belonging to external subsets. Futhermore, most social network mining algorithms comprise a local exploration of the underlying graph, which consists in referencing nodes in the neighborhood of a particular node. The idea of this paper is to use the community structure during the storage of large graphs that arise in social network mining. The goal is to reduce cache misses and consequently, execution time. After formalizing the problem of social network ordering as a problem of optimal linear arrangement which is known as NP-Complet, we propose NumBaCo, a heuristic based on the community structure. We present for Katz score and Pagerank, simulations that compare classic data structures Bloc and Yale to their corresponding versions that use NumBaCo. Results on a 32 cores NUMA machine using amazon, dblp and web-google datasets show that NumBaCo allows to reduce from 62% to 80% of cache misses and from 15% to 50% of execution time. L'une des propriétés des graphes sociaux est leur structure en communautés, c'est-à-dire en sous-ensembles où les noeuds ont une forte densité de liens entre eux et une faible den-sité de liens avec l'extérieur. Par ailleurs, la plupart des algorithmes de fouille des réseaux sociaux comportent une exploration locale du graphe sous-jacent, ce qui amène à partir d'un noeud, à faire référence aux noeuds situés dans son voisinage. L'idée de cet article est d'exploiter la structure en communautés lors du stockage des grands graphes qui surviennent dans la fouille des réseaux so-ciaux. L'objectif est de réduire le nombre de défauts de cache avec pour conséquence l'amélioration du temps d'exécution. Après avoir formalisé le problème de numérotation des noeuds des réseaux sociaux comme un problème d'arrangement linéaire optimal qui est connu comme NP-Complet, nous proposons NumBaCo, une heuristique basée sur la struture en communautés. Nous présentons pour le score de Katz et Pagerank, des simulations comparant les structures de données classiques Bloc et Yale à leurs versions exploitant NumBaCo. Les résultats obtenus sur une machine NUMA de 32 coeurs à partir des jeux de données amazon, dblp et web-google montrent que NumBaCo contribue à diminuer les défauts de cache de 62% à 80% et le temps d'exécution de 15% à 50%.

Magnetic eigenmaps for community detection in directed networks.

Communities in directed networks have often been characterized as regions with a high density of links, or as sets of nodes with certain patterns of connection. Our approach for community detection combines the optimization of a quality function and a spectral clustering of a deformation of the combinatorial Laplacian, the so-called magnetic Laplacian. The eigenfunctions of the magnetic Laplacian, which we call magnetic eigenmaps, incorporate structural information. Hence, using the magnetic eigenmaps, dense communities including directed cycles can be revealed as well as "role" communities in networks with a running flow, usually discovered thanks to mixture models. Furthermore, in the spirit of the Markov stability method, an approach for studying communities at different energy levels in the network is put forward, based on a quantum mechanical system at finite temperature.

The structure of communities in scale‐free networks

SummaryScale‐free networks are often used to model a wide range of real‐world networks, such as social, technological, and biological networks. Understanding the structure of scale‐free networks evolves into a big data problem for business, management, and protein function prediction. In the past decade, there has been a surge of interest in exploring the properties of scale‐free networks. Two interesting properties have attracted much attention: the assortative mixing and community structure. However, these two properties have been studied separately in either theoretical models or real‐world networks. In this paper, we show that the structural features of communities are highly related with the assortative mixing in scale‐free networks. According to the value of assortativity coefficient, scale‐free networks can be categorized into assortative, disassortative, and neutral networks, respectively. We systematically analyze the community structure in these three types of scale‐free networks through six metrics: node embeddedness, link density, hub dominance, community compactness, the distribution of community sizes, and the presence of hierarchical communities. We find that the three types of scale‐free networks exhibit significant differences in these six metrics of community structures. First, assortative networks present high embeddedness, meaning that many links lying within communities but few links lying between communities. This leads to the high link density of communities. Second, disassortative networks exhibit great hubs in communities, which results in the high compactness of communities that nodes can reach each other via short paths. Third, in neutral networks, a big portion of links act as community bridges, so they display sparse and less compact communities. In addition, we find that (dis)assortative networks show hierarchical community structure with power‐law‐distributed community sizes, while neutral networks present no hierarchy. Understanding the structure of communities from the angle of assortative mixing patterns of nodes can provide insights into the network structure and guide us in modeling information propagation in different categories of scale‐free networks. Copyright © 2016 John Wiley & Sons, Ltd.

Cascading failures in coupled networks with both inner-dependency and inter-dependency links.

We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.

A geographic routing approach for IPv6 in large-scale low-power and lossy networks

In this paper, we propose GeoRank, a geographic routing approach for the IPv6-enabled large-scale low-power and lossy networks. We discuss the main drawbacks of the RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) for P2P (point-to-point) communication in large-scale 6LoWPAN networks. Then, we address such drawbacks by proposing a routing protocol, named GeoRank, which integrates RPL protocol with the position-based routing protocol GOAFR (Greedy Other Adaptive Face Routing). The results obtained with simulations show that GeoRank finds shorter routes than RPL in high link density conditions and than GOAFR in low link density conditions. Thus, GeoRank shows to be adaptive to variable link densities found in large-scale networks. Further, GeoRank avoids the use of DAO (Destination Advertisement Object) control messages required in RPL, while being more scalable in terms of memory usage than storing-mode RPL.

Percolation on interacting networks with feedback-dependency links.

When real networks are considered, coupled networks with connectivity and feedback-dependency links are not rare but more general. Here, we develop a mathematical framework and study numerically and analytically the percolation of interacting networks with feedback-dependency links. For the case that all degree distributions of intra- and inter- connectivity links are Poissonian, we find that for a low density of inter-connectivity links, the system undergoes from second order to first order through hybrid phase transition as coupling strength increases. It implies that the average degree k of inter-connectivity links has a little influence on robustness of the system with a weak coupling strength, which corresponds to the second order transition, but for a strong coupling strength corresponds to the first order transition. That is to say, the system becomes robust as k increases. However, as the average degree k of each network increases, the system becomes robust for any coupling strength. In addition, we find that one can take less cost to design robust system as coupling strength decreases by analyzing minimum average degree kmin of maintaining system stability. Moreover, for high density of inter-connectivity links, we find that the hybrid phase transition region disappears, the first order region becomes larger and second order region becomes smaller. For the case of two coupled scale-free networks, the system also undergoes from second order to first order through hybrid transition as the coupling strength increases. We find that for a weak coupling strength, which corresponds to the second order transitions, feedback dependency links have no effect on robustness of system relative to no-feedback condition, but for strong coupling strength which corresponds to first order or hybrid phase transition, the system is more vulnerable under feedback condition comparing with no-feedback condition. Thus, for designing resilient system, designers should try to avoid the feedback dependency links, because the existence of feedback-dependency links makes the system extremely vulnerable and difficult to defend.

Quality control of rain gauge measurements using telecommunication microwave links

Accurate rain rate measurements are essential for many hydrological applications. Although rain gauge remains the reference instrument for the measurement of rain rate, the strong spatial and temporal variability of rainfall makes it difficult to spot faulty rain gauges. Due to the poor spatial representativeness of the point rainfall measurements, this is particularly difficult where their density is low. Taking advantage of the high density of telecommunication microwave links in urban areas, a consistency check is proposed to identify faulty rain gauges using nearby microwave links. The methodology is tested on a data set from operational rain gauges and microwave links, in Zurich (Switzerland). The malfunctioning of rain gauges leading to errors in the occurrence of dry/rainy periods are well identified. In addition, the gross errors affecting quantitative rain gauge measurements during rainy periods, such as blocking at a constant value, random noise and systematic bias, can be detected. The proposed approach can be implemented in real time.

A Bayesian approach to finding groups in a food web

A food web describes the feeding links between species in a community. The species in many food webs are organized into groups of highly linked species that are weakly linked to species in other groups. A Bayesian approach to identifying such groups in an observed food web is described. This approach extends a previous non-Bayesian one that does not exploit information about the relatively high density of links within groups and relatively low density between groups. Under the new approach, this information is encoded through prior distributions for within- and between-group link densities. The approach is shown to work well on simulated food webs. Results are presented of the application of the method to the Coachella Valley desert food web.

Cluster synchronization in nonlinearly coupled delayed networks of non-identical dynamic systems

In the real world, many networks show community structure, i.e., clusters of nodes, which have a high density of links within the same cluster but a lower density of links between different clusters. In this paper, nonlinearly coupled networks with community structure and non-identical nodes and with time-varying delay are considered. By applying pinning control to a fraction of network nodes, and using a suitable Lyapunov function, we obtain some new and useful synchronization criteria, which guarantee that various clusters are synchronized independently. An example is presented to show the application of the criteria obtained in this paper.

Farming practices change food web structures in cereal aphid–parasitoid–hyperparasitoid communities

Agricultural intensification has been shown to result in a decline in biodiversity across many taxa, but the changes in community structure and species interactions remain little understood. We have analysed and compared the structure of feeding interactions for cereal aphids and their primary and secondary parasitoids in organically and conventionally managed winter wheat fields using quantitative food web metrics (interaction evenness, generality, vulnerability, link density). Despite little variation in the richness of each trophic group, food web structures between the two farming systems differed remarkably. In contrast to common expectations, aphids and primary parasitoids were characterized by (1) a higher evenness of interaction frequencies (interaction evenness) in conventional fields, which cascaded to interactions at the next trophic level, with (2) a higher interaction evenness, (3) a higher ratio of primary parasitoid taxa per secondary parasitoid (generality) and (4) a higher link density. Aphid communities in the organically managed fields almost exclusively consisted of a single ear-colonizing species, Sitobion avenae, while highly fertilized conventional fields were mainly infested by leaf-colonizing aphids that benefit from the nutritional status of winter wheat. In conclusion, agricultural intensification appears to foster the complexity of aphid–parasitoid food webs, thereby not supporting the general expectation on the importance of organic farming practices for species richness and food web complexity.Electronic supplementary materialThe online version of this article (doi:10.1007/s00442-012-2387-8) contains supplementary material, which is available to authorized users.

Critical effect of dependency groups on the function of networks.

Current network models assume one type of links to define the relations between the network entities. However, many real networks can only be correctly described using two different types of relations. Connectivity links that enable the nodes to function cooperatively as a network and dependency links that bind the failure of one network element to the failure of other network elements. Here we present an analytical framework for studying the robustness of networks that include both connectivity and dependency links. We show that a synergy exists between the failure of connectivity and dependency links that leads to an iterative process of cascading failures that has a devastating effect on the network stability. We present exact analytical results for the dramatic change in the network behavior when introducing dependency links. For a high density of dependency links, the network disintegrates in a form of a first-order phase transition, whereas for a low density of dependency links, the network disintegrates in a second-order transition. Moreover, opposed to networks containing only connectivity links where a broader degree distribution results in a more robust network, when both types of links are present a broad degree distribution leads to higher vulnerability.

Generation of clusters in complex dynamical networks via pinning control

Many real-world networks show community structure, i.e., groups (or clusters) of nodes that have a high density of links within them but with a lower density of links between them. In this paper, by applying feedback injections to a fraction of network nodes, various clusters are synchronized independently according to the community structure generated by the group partition of the network (cluster synchronization). This control is achieved by pinning (i.e. applying linear feedback control) to a subset of the network nodes. Those pinned nodes are selected not randomly but according to the topological structure of communities of a given network. Specifically, for a given group partition of a network, those nodes with direct connections between groups must be pinned in order to achieve cluster synchronization. Both the local stability and global stability of cluster synchronization are investigated. Taking the tree-shaped network and the most modular network as two particular examples, we illustrate in detail how the pinning strategy influences the generation of clusters. The simulations verify the efficiency of the pinning schemes used in this paper.