Low Edge Density Research Articles

Community detection with Greedy Modularity disassembly strategy

Community detection recognizes groups of densely connected nodes across networks, one of the fundamental procedures in network analysis. This research boosts the standard but locally optimized Greedy Modularity algorithm for community detection. We introduce innovative exploration techniques that include a variety of node and community disassembly strategies. These strategies include methods like non-triad creating, feeble, random as well as inadequate embeddedness for nodes, as well as low internal edge density, low triad participation ratio, weak, low conductance as well as random tactics for communities. We present a methodology that showcases the improvement in modularity across the wide variety of real-world and synthetic networks over the standard approaches. A detailed comparison against other well-known community detection algorithms further illustrates the better performance of our improved method. This study not only optimizes the process of community detection but also broadens the scope for a more nuanced and effective network analysis that may pave the way for more insights as to the dynamism and structures of its functioning by effectively addressing and overcoming the limitations that are inherently attached with the existing community detection algorithms.

In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks

The St. Nicolas House algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.

Identification of I-mode with ion ITB in NBI-heated plasmas on the HL-2A tokamak

Improved energy confinement mode (I-mode) has been observed on the HL-2A tokamak. The I-mode features an edge transport barrier (ETB) in electron temperature and a low confinement mode like edge density. A weakly coherent mode (WCM) is observed in the edge region with the frequency of kHz and the poloidal wavenumber of . The maximum of WCM amplitude is located near the top of electron temperature pedestal. A critical value of velocity shear for triggering the L–I transition has been found, and is much lower than that for triggering the L–H transition. Additionally, ion internal transport barrier (ITB) has been observed in the I-mode. The formation of ion ITB is due to the increase of velocity shear, leading to the suppression of turbulence. Transport analysis further confirms the existence of electron ETB and ion ITB. The coexistence of electron ETB and ion ITB leads to an improved plasma confinement, which is comparable to that in the high confinement mode, suggesting that I-mode with ITB regimes could be an interesting operation scenario for future fusion devices.

Agriculture intensity and landscape configuration influence the spatial use of wildcats across Europe

Land use intensification is increasing worldwide and affects wildlife movements, particularly of specialist carnivores. Resource availability and anthropogenic activities drive the extent and shape of home range size. Wildlife may respond to decreased resource availability under intensification scenarios by increasing their home ranges; however they may be less affected when inhabiting sustainable agricultural landscapes. We investigate whether agricultural practices and landscape configuration influence the spatial behaviour of wildcats, a medium-sized specialist carnivore inhabiting landscapes with different degrees of agricultural presence across Europe. We focus on the effect of the proportions of high impact and low impact agriculture, forest integrity and forest edge density on wildcat home range size. We found that wildcat home range increased along with the proportion of high impact agriculture and the forest integrity, whereas it decreased when forest edge density increased. Forest edge density buffered the detrimental effects caused by high impact agriculture. To enhance the long term conservation of wildcats in Europe it is crucial to protect the sustainable mosaic-structured landscapes and prevent its conversion to homogenous intensified agricultural landscapes.

Spatial predictors and species’ traits: evaluating what really matters for medium‐sized and large mammals in the Atlantic Forest, Brazil

Abstract Habitat loss and the conversion of natural environments to anthropogenic land uses are among the main drivers of biodiversity decline worldwide. The remaining habitats are inserted in highly modified landscapes, presenting contrasting patch and landscape features. Understanding species’ responses to these anthropogenic land‐use changes is essential for informing conservation planning. We evaluate which spatial predictors (measured at the patch and landscape scales) and species’ life‐history traits best predict the occurrence of medium‐sized and large mammals in forest fragments of the threatened Atlantic Forest of South America. We gathered occurrence data for 36 medium‐sized and large mammal species recorded by camera traps, distributed over 220 forest fragments spanning the entire Atlantic Forest biome. Species were classified according to their characteristics as follows: ‘hunted forest‐dependent’, ‘non‐hunted forest‐dependent’, and ‘generalist’ species. Further, each species’ occurrence was related to spatial predictors and life‐history trait variables. We revealed a severe defaunation of forest mammals occurring in most forest fragments in the Atlantic Forest. Landscapes containing large forest patches, low road density, and high human population density harboured high numbers of mammal species, including those exhibiting greater body mass. Nevertheless, mammal groups diverge in their sensitivity to landscape changes. Hunted forest‐dependent species mainly occurred in landscapes with lower edge density and numbers of patches and with less anthropogenic disturbance, while non‐hunted forest‐dependent species mainly occupied large forest fragments. Finally, generalist species also occurred in landscapes with low edge density and number of patches, but were more prevalent in landscapes with higher human population density. We stress the importance of maintaining large forest fragments, considering the effects of landscape configuration on conservation strategies, and reducing anthropogenic disturbances to ensure a greater persistence of mammal species in the Atlantic Forest.

Landscape Structure of Woody Cover Patches for Endangered Ocelots in Southern Texas

Few ecological studies have explored landscape suitability using the gradient concept of landscape structure for wildlife species. Identification of conditions influencing the landscape ecology of endangered species allows for development of more robust recovery strategies. Our objectives were to (i) identify the range of landscape metrics (i.e., mean patch area; patch and edge densities; percent land cover; shape, aggregation, and largest patch indices) associated with woody vegetation used by ocelots (Leopardus pardalis), and (ii) quantify the potential distribution of suitable woody cover for ocelots across southern Texas. We used the gradient concept of landscape structure and the theory of slack combined with GPS telemetry data from 10 ocelots. Spatial distribution of high suitable woody cover is comprised of large patches, with low shape-index values (1.07–2.25), patch (27.21–72.50 patches/100 ha), and edge (0–191.50 m/ha) densities. High suitability landscape structure for ocelots occurs in 45.27% of woody cover in southern Texas. Our study demonstrates a new approach for measuring landscape suitability for ocelots in southern Texas. The range of landscape values identified that there are more large woody patches containing the spatial structure used by ocelots than previously suspected, which will aid in evaluating recovery and road planning efforts.

Landscape pattern change simulations in Tibet based on the combination of the SSP-RCP scenarios

Monitoring landscape pattern change can provide spatial explicit basis for future landscape management. The future socioeconomic and climate change drivers should be systematically combined in landscape pattern monitoring, while they are often regarded as independent parameters in landscape monitoring models. This study sought to project the detailed landscape pattern change based on landscape composition and configuration in Tibet by 2030, and combined the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). The results showed area of the unused land and forest will reduce by a minimum standard of 11.42 × 104 and 9.04 × 104 km2 from 2010 to 2030, respectively. Other land use types will increase, and the highest increase in grassland will be 9.30 × 105 km2. Combined SSP1 and RCP2.6 scenario show high landscape aggregation and low edge density on cultivated land, urban land and grassland in Tibet as a whole. However, in typical cultivated and urban landscape, the abovementioned rule is appeared in the combined SSP4 and RCP6.0 scenario. These findings stress the importance of systematically modeling the socioeconomic demand and climate change in landscape pattern monitoring, and using both landscape composition and configuration indexes for scenario evaluation.

Edge density affects demography of an exploited grassland bird

AbstractHumans modify landscapes for a variety of reasons including agriculture, urbanization, and to facilitate recreation. Landscape modifications can lead to complete loss of existing cover types and/or fragmentation of remaining cover types. Grasslands are one of the ecosystems most heavily impacted by human modifications; however, for many species, an understanding of how fragmentation affects population parameters is lacking. We investigated the influence of landscape fragmentation on survival, reproduction, and density using the northern bobwhite (Colinus virginianus), a facultative grassland species, as our case study. We experimentally manipulated the level of edge density (none, low, and high) of bobwhite management units and estimated seasonal survival, productivity, and autumn densities. Winter survival was lower in edge density treatments, relative to the control. An increase in migratory raptor density during the winter, along with a decrease in thermal refugia, may be contributing to lower winter survival in edge density treatments. Fecundity was higher in the edge density treatments, relative to the control, and was negatively related to winter survival. Similarly, breeding season survival was higher in more fragmented sites. This indicated a density‐dependent relationship, where lower bird densities at the beginning of the breeding season possibly led to greater resource availability, and thus survival and productivity, of the birds that remained. However, a density‐dependent response in breeding season survival and productivity was not enough to offset the negative impact of edges in the low edge density sites. Consequently, autumn density was lower relative to both control and high edge density treatments. Our study demonstrates the importance of capturing the entire life cycle when exploring the relationship between landscape pattern and population parameters.

Geometric graphs from data to aid classification tasks with Graph Convolutional Networks

SummaryTraditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here, we show that, even if additional relational information is not available in the dataset, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world datasets from various scientific domains.

A new formulation for the Weighted Safe Set Problem

Given a connected graph G = (V, E), a Safe Set S is a subset of the vertex set V such that the cardinality of each connected component in the subgraph induced by V \\ S does not exceed the cardinality of any connected component in the subgraph induced by S, whenever there is an edge in G between vertices of the two components. When the vertices of G are weighted, the weight of a component is defined as the sum of the weights of its vertices, and the notion of safe set is extended by considering the weight of connected components in subgraphs induced by S and by V \\ S. We propose an integer linear formulation for the Weighted Safe Set Problem that uses only one variable per vertex. The formulation has an exponential number of constraints, which can be generated on-the-fly within a branch-and-cut algorithm. We describe a linear-time separation algorithm for these constraints. In addition, we describe families of cuts based on cliques and on minimum weight cut separators, and discuss separation algorithms. A branch-and-cut algorithm that solves the proposed formulation is computationally compared with two alternative formulations from the literature, and shows faster in solving most of benchmark instances with low edge density.

Forest Availability and Fragmentation Drive Movement Behaviour of Wintering European Honey-Buzzard Pernis apivorus in Africa

Decreases in suitable habitat due to loss and fragmentation on the African non-breeding grounds are believed to be a driver of the declines in Palaearctic migrants. However, the effects of habitat loss and fragmentation on individual migrants have rarely been investigated. The European Honey-buzzard Pernis apivorus, a migratory forest specialist, displays the characteristics of a species that would be sensitive to declines in suitable habitat. We assessed site fidelity, habitat selection and home range characteristics of 32 tracked European Honey-buzzards in Africa to understand how habitat transformation on the non-breeding grounds affects the movement behaviour (particularly habitat use and home range characteristics) of the species. Adults had greater site fidelity than juveniles, indicating that young birds were exploring to locate suitable habitat for future non-breeding territories. Besides site fidelity, there were few significant differences between the movements of adult and juvenile Honey-buzzards. Birds of all age classes strongly selected treed habitat, as expected. Treed largest patch index and edge density had a negative effect on home range size, demonstrating that Honey-buzzards rely on large tracts of continuous forest, but can tolerate some level of fragmentation. Individuals with larger home ranges due to greater levels of forest fragmentation moved greater daily distances. In addition, Honey-buzzards spent more time in home ranges with larger treed largest patch indices and lower edge density. This reveals that birds that do not have access to a large patch of unfragmented forest are both flying greater distances on a daily basis and moving to new home ranges more often. This likely results in greater energy expenditure over their non-breeding period, which may have adverse effects on their long-term survival. Furthermore, our analyses support the hypothesis that continuing forest loss in Africa will have a negative effect on the European Honey-buzzard population.

Geometric Graphs from Data to Aid Classification Tasks with Graph Convolutional Networks

Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here we show that, even if additional relational information is not available in the data set, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world data sets from various scientific domains.

Spatial metrics from LiDAR roof mapping for fire spread risk assessment of informal settlements in Cape Town, South Africa

The risk of fire spread in informal settlements is significant and can be analysed as a function of the spatial arrangement of dwellings. Spatial metrics representing density and shape of dwellings are proposed as a method to identify settlements at high risk of fire spread. LiDAR data is used to map dwelling roofs for informal settlements in the City of Cape Town, South Africa. The LiDAR roof dataset is validated against a visually interpreted dataset digitised from 6 cm resolution aerial photography and is found to have an overall completeness and correctness accuracy of greater than 75% with systematic underrepresentation of roofs in the LiDAR dataset. Correlation analysis of metrics derived from the LiDAR dataset and the reference dataset indicates that only the edge density and landscape density metrics could be applied with confidence to all the settlements. These two metrics are then applied to the informal settlements of Kosovo and Imizamo Yethu. A high landscape density in combination with a low edge density is found to be indicative of fire spread risk. This study represents a first step in the development of spatial metrics for understanding informal settlement fire spread risk.

Turbulence driven widening of the near-SOL power width in ASDEX Upgrade H-Mode discharges

Divertor heat flux data from infra red (IR) from various tokamaks in H-Mode regime show that the power width, , scales approximately like 1.6 (R/a) ρs,pol. However, the IR based scaling comes with the restriction that only discharges were considered with attached divertor conditions. Here we set up a new data base covering low edge density data but also including high density plasmas reaching the H-mode density limit. We use Thomson-scattering to measure the electron temperature decay length which will set the near-SOL power width through parallel heat conduction. We present scaling results for the density, temperature and pressure separatrix fall-off lengths. As the principal result we derive a generalized power width scaling which reads as where describes a normalized collisionality (). The parameter describes the relative importance of the interchange effect on drift-wave turbulence as proposed by Scott and is found to be closely linked to the diamagnetic parameter αd introduced by Rogers, Drake and Zeiler (RDZ). The edge operational space for ASDEX Upgrade displays a boundary qualitatively in agreement with the region labelled as density limit in the edge plasma phase space proposed by RDZ. The data base shows in the limit of low edge densities () accurate agreement to the IR based power width scaling. At elevated separatrix densities () the power width is broadened by a factor of up to about three accompanied by a reduction of the H98,y2 confinement factor. Plasmas with higher triangularity show higher values for H98,y2 in the same range of normalized collisionality.

Modeling ExB drift transport in conceptual slot divertor configurations

AbstractAt DIII‐D, a slot divertor concept, called small‐angle‐slot (SAS), is under development, aiming to enable detachment at relatively low plasma edge density. We report on simulations using the SOLPS‐ITER two‐dimensional edge code to examine the performance of conceptual “SAS 2” slot configurations. The focus of the analysis is on E × B drift effects on upstream density at detachment (UDD), with detachment marked by electron temperature Te ≤ 3 eV at the outer strike point (OSP). With toroidal field such that radial E × B drift carries particles from the OSP towards the private flux region (PFR), placing the OSP near the inner slot wall gives ≈20% lower UDD than having the OSP near the outer wall. The inner wall effectively traps the radial E × B drift flux, resulting in low Te and associated radial electric field in the PFR, and thus small losses from the slot to the inner target via poloidal E × B drift flux. With toroidal field reversed such that radial E × B drift is reversed, OSP placement near the inner wall gives ≈10% lower UDD than OSP placement near the outer wall. Although radial E × B flux is from the OSP towards the outer wall, this flux largely escapes the slot, raising the UDD. A change in the slot shaping is suggested with the goal of eliminating such E × B‐driven particle losses from the slot.

Dietary and habitat niche partitioning in congeneric cryptobenthic reef fish species

Ecological niches hold critical information concerning the eco-evolutionary dynamics that govern biodiversity and abundance patterns. Cryptobenthic reef fishes account for approximately half of all reef fish species and are an abundant and important group on coral reefs worldwide. Yet, due to their small size and inconspicuous lifestyles, relatively little is known about the ecological niches of most cryptobenthic species. Here, we use gut content DNA metabarcoding to determine dietary niche overlap and prey richness in four sympatric species of cryptobenthic reef fishes in two genera (Acanthemblemaria aspera, A. spinosa, Enneanectes altivelis, and E. matador). Furthermore, we test whether dietary differentiation corresponds with differences in species distribution patterns across twelve sites on the Mesoamerican Barrier Reef in Belize. Our approach reveals dietary partitioning among the four species, which is further supported by low edge density and high modularity in the resulting trophic network. A. spinosa and E. matador consume a significantly higher richness of prey items than their congeners. This result corresponds with non-random distributions and co-occurrence patterns in both species pairs: the two high prey richness species (A. spinosa and E. matador) co-occur more frequently than predicted by chance, but they are exclusive to exposed forereef sites with high wave action. In contrast, their congeners occur across exposed forereef and sheltered backreef sites, but they do not increase in numbers at sheltered sites. Our findings suggest that A. spinosa and E. matador monopolize a wide variety of prey in exposed habitats, but they are unable to meet the energetic demands of their adaptation to high-flow habitats in sheltered areas, possibly due to lower prey availability. This, in turn, indicates strong ecological differentiation among closely related species of cryptobenthic fishes, driven by links between diet, physiology, prey availability, and wave exposure.

Scrape-off layer transport and filament characteristics in high-density tokamak regimes

A detailed cross-device investigation on the role of filamentary dynamics in high-density regimes has been performed within the EUROfusion framework, comparing the ASDEX Upgrade (AUG) and TCV tokamaks. Both devices run density ramp experiments at different levels of plasma current, keeping the toroidal field or q95 constant in order to disentangle the role of the parallel connection length and the current. During the scan at a constant toroidal field, in both devices the scrape-off layer (SOL) profiles tend to develop a clear SOL density shoulder at a lower edge density whenever the current is reduced. Different current behaviour is substantially reconciled in terms of the edge density normalized to the Greenwald fraction. During the scan at constant q95 AUG exhibits similar behaviour, whereas in TCV no upstream profile modification signature has been observed at lower current levels. The latter behaviour has been ascribed to the lack of target density rollover. The relation between the upstream density profile modification and detachment condition has been investigated. For both devices the relation between blob size and the SOL density e-folding length is found independent of the plasma current, with the observation of a clear increase in blob size and the edge density normalized to a Greenwald fraction. ASDEX Upgrade has also explored filamentary behaviour in the H-mode. The experiments in AUG have focused on the role of neutrals, performing discharges with and without cryogenic pumps, highlighting how high neutral pressure, not only in the divertor but also at the midplane, is needed in order to develop an H-mode SOL profile shoulder in AUG.

Local and landscape drivers of the number of individuals and genetic diversity of a microendemic and critically endangered salamander

Conversion of forest ecosystems to human-modified landscapes threatens the persistence of forest-specialist species. However, the local and landscape drivers of population abundance and genetic diversity of these species are largely unknown, especially for elusive and critically endangered species, such as the salamander Pseudoeurycea robertsi—a species microendemic to the Nevado de Toluca volcano, Mexico. We assessed the relative influence of local- and landscape-scale habitat amount and habitat spatial configuration on the number of individuals and genetic diversity of P. robertsi. Given its low vagility, we expected stronger responses to local habitat amount than to landscape variables, with habitat configuration showing the weakest effects on all responses. Using multiscale and multimodel inference approaches, we tested the relative effect of local habitat amount (fallen logs volume), landscape habitat amount (forest cover) and landscape configuration (forest edge density and forest fragmentation per se) on the number of salamanders and its genetic diversity. The number of individuals was more strongly related to local and landscape variables than genetic diversity. As predicted, local habitat amount showed stronger positive effects on number of individuals and number of alleles than forest cover. In addition, all response variables increased in landscapes with lower edge density. Fragmentation per se showed weak influence on all responses. Fallen logs volume is a major driver of this forest-specialist salamander. Yet, edge density also shapes salamander populations, especially the number of individuals. Retaining fallen logs in forests and increasing forest core areas are critical for salamander conservation.

Mining Highly Reliable Dense Subgraphs from Uncertain Graphs

The uncertainties of the uncertain graph make the traditional definition and algorithms on mining dense graph for certain graph not applicable. The subgraph obtained by maximizing expected density from an uncertain graph always has many low edge-probability data, which makes it low reliable and low expected edge density. Based on the concept of β-subgraph, to overcome the low reliability of the densest subgraph, the concept of optimal β-subgraph is proposed. An efficient greedy algorithm is also developed to find the optimal β-subgraph. Simulation experiments of multiple sets of datasets show that the average edge-possibility of optimal β-subgraph is improved by nearly 40%, and the expected edge density reaches 0.9 on average. The parameter β is scalable and applicable to multiple scenarios.

White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8–12 years. In addition to conventional diffusion tensor imaging (DTI) maps – including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps – evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.