Search

Background: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. Methods: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. Results: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. Conclusions: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions. [ABSTRACT FROM AUTHOR]

The input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis. [ABSTRACT FROM AUTHOR]

Key class identification approaches aim at identifying the most important classes to help developers, especially newcomers, start the software comprehension process. So far, many supervised and unsupervised approaches have been proposed; however, they have not considered the effort to comprehend classes. In this article, we identify the challenge of "effort-aware key class identification"; to partially tackle it, we propose an approach, EASE, which is implemented through a modification to existing unsupervised key class identification approaches to take into consideration the effort to comprehend classes. First, EASE chooses a set of network metrics that has a wide range of applications in the existing unsupervised approaches and also possesses good discriminatory power. Second, EASE normalizes the network metric values of classes to quantify the probability of any class to be a key class and utilizes Cognitive Complexity to estimate the effort required to comprehend classes. Third, EASE proposes a metric, RKCP, to measure the relative key-class proneness of classes and further uses it to sort classes in descending order. Finally, an effort threshold is utilized, and the top-ranked classes within the threshold are identified as the cost-effective key classes. Empirical results on a set of 18 software systems show that (i) the proposed effort-aware variants perform significantly better in almost all (≈98.33%) the cases, (ii) they are superior to most of the baseline approaches with only several exceptions, and (iii) they are scalable to large-scale software systems. Based on these findings, we suggest that (i) we should resort to effort-aware key class identification techniques in budget-limited scenarios; and (ii) when using different techniques, we should carefully choose the weighting mechanism to obtain the best performance. [ABSTRACT FROM AUTHOR]

Animal social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses.We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations and is fully compatible with popular methods used in social network analysis.We show how the framework can be applied to common types of data and how various types of downstream statistical analyses can be performed, including non‐random association tests and regressions on network properties.Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made both an R package and example R scripts available to enable adoption of the framework. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

International seed trade networks (iSTNs) are complex networks formed by the trade of seeds between economies. We investigate the statistical properties of the international trade networks for maize seeds and rice seeds from the viewpoint of temporal directed and weighted networks. We find that most properties, including node and edge numbers, average in- and out-degrees, total and average trade values, network density and clustering coefficient, showed an increasing trend, which indicates that the increasing globalization of seed trade has led to more and more complete and dense trade networks. We also find that links with larger trade values have greater stability. In addition, we observe positive correlations between in-degree and out-degree, link reciprocity and in- and out-degrees, link reciprocity and in- and out-strengths and clustering coefficient and in- and out-degrees. In contrast, there are negative correlations between clustering coefficient and in- and out-strengths. Furthermore, we find that both networks are assortatively mixed in most years, which indicates that large exporters and importers are more likely to trade with other large exporters and importers. The structural properties of the two iSTNs of maize and rice share many qualitative similarities, but exhibit quantitative differences. [ABSTRACT FROM AUTHOR]

Plants and their insect herbivores have been a dominant component of the terrestrial ecological landscape for the past 410 million years and feature intricate evolutionary patterns and co‐dependencies. A complex systems perspective allows for both detailed resolution of these evolutionary relationships as well as comparison and synthesis across systems. Using proxy data of insect herbivore damage (denoted by the damage type or DT) preserved on fossil leaves, functional bipartite network representations provide insights into how plant–insect associations depend on geological time, paleogeographical space, and environmental variables such as temperature and precipitation. However, the metrics measured from such networks are prone to sampling bias. Such sensitivity is of special concern for plant–DT association networks in paleontological settings where sampling effort is often severely limited. Here, we explore the sensitivity of functional bipartite network metrics to sampling intensity and identify sampling thresholds above which metrics appear robust to sampling effort. Across a broad range of sampling efforts, we find network metrics to be less affected by sampling bias and/or sample size than richness metrics, which are routinely used in studies of fossil plant–DT interactions. These results provide reassurance that cross‐comparisons of plant–DT networks offer insights into network structure and function and support their widespread use in paleoecology. Moreover, these findings suggest novel opportunities for using plant–DT networks in neontological terrestrial ecology to understand functional aspects of insect herbivory across geological time, environmental perturbations, and geographic space. [ABSTRACT FROM AUTHOR]

Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers' comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers' processing of the text. Results showed that low ability and high ability readers' scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension. [ABSTRACT FROM AUTHOR]

The cold-start problem is a condition for a new node to join a network with no available information or an isolated node. Most studies use topological network information with the Triadic Closure principles to predict links in future networks. However, the method based on the Triadic Closure principles cannot predict the future link due to no common neighbors between the predicted node pairs. Adamic Adar is one of the methods based on the Triadic Closure principles. This paper proposes three methods for extending Adamic Adar based on network metrics. The main objective is to utilize the network metrics to attract the isolated node or new node to make new relationships in the future network. The proposed method is called the extended Adamic Adar index based on Degree Centrality (DCAA), Closeness Centrality (CloCAA), and Clustering Coefficient (CluCAA). Experiments were conducted by sampling 10% of the dataset as testing data. The proposed method is examined using the four real-world networks by comparing the AUC score. Finally, the experiment results show that the DCAA and CloCAA can predict up to 99% of node pairs with a cold-start problem. DCAA and CloCAA outperform the benchmark, with an AUC score of up to 0,960. This finding shows that the extended Adamic Adar index can overcome prediction failures on node pairs with cold-start problems. In addition, prediction performance is also improved compared to the original Adamic Adar. The experiment results are promising for future research due to successfully improving the prediction performance and overcoming the cold-start problem. [ABSTRACT FROM AUTHOR]

To study whether Tai Chi (TC) practice can improve the brain connectivity of the prefrontal lobe of college students, the positive psychological capital questionnaires and resting EEG signals were acquired from 50 college students including 25 TC practitioners and 25 demographically matched TC healthy controls. The results showed that the score of the positive psychological capital questionnaire of the TC group was significantly higher than that of the control group, and the node degree of the frontal lobe and temporal lobe of both groups was positively correlated with the score of the positive psychological capital questionnaire. In addition, the response time of the TC group under auditory stimulation was significantly shorter than that of the control group, and there was a significant positive correlation between response time and its characteristic path length, and a significant negative correlation with global efficiency. Meanwhile, during the selected range of sparsity, the difference in global network parameters between two groups is significant in the alpha band. Under all single sparsity, the clustering coefficient, global efficiency, and local efficiency of the TC group have a higher trend, while the characteristic path length tended to be shorter. In the analysis of the local characteristics of the resting brain functional network, it was found that the node degree of the frontal lobe and temporal lobe of the TC group was higher, and the difference was significant in some nodes. These results all point to the fact that TC practice has a certain impact on specific brain areas of the brain. [ABSTRACT FROM AUTHOR]

Tau neurofibrillary tangles have a central role in the pathogenesis of Alzheimer's Disease (AD). Mounting evidence indicates that the propagation of tau is assisted by brain connectivity with weakened white-matter integrity along the propagation pathways. Recent advances in tau positron emission tomography tracers and diffusion magnetic resonance imaging allow the visualization of tau pathology and white-matter connectivity of the brain in vivo. The current study aims to investigate how tau deposition and structural connectivity are associated with memory function in prodromal AD. In this study, tau accumulation and structural connectivity data from 83 individuals (57 cognitively normal participants and 26 participants with mild cognitive impairment) were associated with neurocognitive test scores. Statistical analyses were performed in 70 cortical/subcortical brain regions to determine: 1. the level of association between tau and network metrics extracted from structural connectivity and 2. the association patterns of brain memory function with tau accumulation and network metrics. The results showed that tau accumulation and network metrics were correlated in early tau deposition regions. Furthermore, tau accumulation was associated with worse performance in almost all neurocognitive tests performance evaluated in the study. In comparison, decreased network connectivity was associated with declines in the delayed memory recall in Craft Stories and Benson Figure Copy. Interaction analysis indicates that tau deposition and dysconnectivity have a synergistic effect on the delayed Benson Figure Recall. Overall, our findings indicate that both tau deposition and structural dysconnectivity are associated with neurocognitive dysfunction. They also suggest that tau-PET may have better sensitivity to neurocognitive performance than diffusion MRI-derived measures of white-matter connectivity. [ABSTRACT FROM AUTHOR]

Background: Anorexia nervosa (AN) is a disorder characterized by an incapacitating fear of weight gain and by a disturbance in the way the body is experienced, facets that motivate dangerous weight loss behaviors. Multimodal neuroimaging studies highlight atypical neural activity in brain networks involved in interoceptive awareness and reward processing. Methods: The current study used resting-state neuroimaging to model the architecture of large-scale functional brain networks and characterize network properties of individual brain regions to clinical measures. Resting-state neuroimaging was conducted in 62 adolescents, 22 (21 female) with a history of AN and 40 (39 female) healthy controls (HCs). Sensorimotor and basal ganglia regions, as part of a 165-region whole-brain network, were investigated. Subject-specific functional brain networks were computed to index centrality. A contrast analysis within the general linear model covarying for age was performed. Correlations between network properties and behavioral measures were conducted (significance q <.05). Results: Compared to HCs, AN had lower connectivity from sensorimotor regions, and greater connectivity from the left caudate nucleus to the right postcentral gyrus. AN demonstrated lower sensorimotor centrality, but higher basal ganglia centrality. Sensorimotor connectivity dyads and centrality exhibited negative correlations with body dissatisfaction and drive for thinness, two essential features of AN. Conclusions: These findings suggest that AN is associated with greater communication from the basal ganglia, and lower information propagation in sensorimotor cortices. This is consistent with the clinical presentation of AN, where individuals exhibit patterns of rigid habitual behavior that is not responsive to bodily needs, and seem "disconnected" from their bodies. Plain English summary: Individuals with anorexia nervosa (AN) usually report a fear of gaining weight. They often develop a dislike and distrust of their bodies, feeling that their bodies had somehow let them down. These fears can in turn lead to dangerous weight loss behaviors. Magnetic resonance imaging of the brain is a tool that helps highlight the underlying biological processes associated with AN. In the current study we aim to investigate how the connections in key regions of the brain are related to clinical and behavioral factors associated with AN. We found regions of two main networks were associated with body dissatisfaction and drive for thinness, which are key features of AN. The brain regions involved help explain why patients with AN have characteristics of feeling disconnected from their bodies, having difficulty labeling and regulating emotions, responding to biological needs such as hunger and fatigue, and differentiating experiences that will be rewarding. These results can help guide interventions that will be directed towards helping individuals with AN to better sense, decipher, and act on the various signals being communicated by their body. [ABSTRACT FROM AUTHOR]

Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole‐brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation‐based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high‐resolution network (~50,000‐nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age‐, sex‐matched healthy subjects (n = 36) underwent high‐resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan–Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE. [ABSTRACT FROM AUTHOR]

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1. [ABSTRACT FROM AUTHOR]

Supply chain networks are complex and often proprietary, which implies that on the most part, the structure of a company's supply chain is not well known nor accessible. This research investigates supply chain network topology, properties and supply network evolution using a data-driven approach. The key idea is to construct a sample set of data from a financial source and examine it in the context of supply network topology. This represents a new direction, since, while financial data has been applied by researchers to explore financial relationships in a supply chain, the application of this data source to determine the underlying topological characteristics is still in its infancy. As a starting point, we create a sample of supply networks from the retail industry sector (two from home improvement industry and one from the sporting goods industry). We expect that the retail industry will provide a rich and dynamic source of representative data for a typical supply chain network. We use the sample data sets to identify specific topological characteristics (for example, average degree, network diameter, average path length, and degree exponent) which help explain the evolution and dynamics of a modern supply network. Using these identified characteristics, our plan is to expand the selection to cover additional networks over a wider time-span in order to generalize the findings. [ABSTRACT FROM AUTHOR]

Copyright of Colombia Forestal is the property of Colombia Forestal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Matching entities between datasets is a crucial step for combining multiple datasets on the semantic web. A rich literature exists on different approaches to this entity resolution problem. However, much less work has been done on how to assess the quality of such entity links once they have been generated. Evaluation methods for link quality are typically limited to either comparison with a ground truth dataset (which is often not available), manual work (which is cumbersome and prone to error), or crowd sourcing (which is not always feasible, especially if expert knowledge is required). Furthermore, the problem of link evaluation is greatly exacerbated for links between more than two datasets, because the number of possible links grows rapidly with the number of datasets. In this paper, we propose a method to estimate the quality of entity links between multiple datasets. We exploit the fact that the links between entities from multiple datasets form a network, and we show how simple metrics on this network can reliably predict their quality. We verify our results in a large experimental study using six datasets from the domain of science, technology and innovation studies, for which we created a gold standard. This gold standard, available online, is an additional contribution of this paper. In addition, we evaluate our metric on a recently published gold standard to confirm our findings. [ABSTRACT FROM AUTHOR]

1. Generalists are assumed to play a key role in structuring and stabilising animal–plant mutualistic networks. Until now, analyses on the effects of generalists have been based on empirical field data or simulations. The present natural field experiment manipulated the abundance of a generalist and abundant stingless bee [Melipona (Eomelipona) marginata] to determine the effects on the mutualistic network. 2. Networks were generated, and associated metrics were used for comparing replicate plots with and without the insertion of stingless bee nests. 3. Unweighted metrics and the basic qualitative structural pattern of networks (high nestedness, very low modularity and specialisation) was not affected by experimental variation in stingless bee abundance because they exert strong basal effects on the plant–pollinator community under natural conditions of abundance. Still, increased abundance caused significant variation in weighted nestedness and modularity and community‐level specialisation. 4. Supporting predictions of neutral models, increased abundance of the stingless bee led to an increase in most of its specific metrics, expressing the expansion of its realised trophic niche. 5. During this process, specialist and other generalist bees were affected in different ways. More plant species became even more dependent on this stingless bee (increased asymmetry). 6. Long‐term effects could not be inferred directly from instantaneous values of network metrics. Nonetheless, the increased abundance of the generalist stingless bee may both reduce the local level of ecological specialisation in the short term and affect the spatial distribution of less abundant and/or specialist bee species and plants in the long term. [ABSTRACT FROM AUTHOR]

The classification of a company into the relevant quotation market is an essential part of stock market efficiency. The aim of the article is to predict the classification of enterprises into the two types of quotation markets on the Warsaw Stock Exchange (i.e. the main one and NewConnect) by means of network measures of the company's participation in interlocking directorates' networks. The research was carried out on a network of enterprises established on the basis of their relationships through shared board directors. This network included 460 companies listed on the main exchange, and 442 companies in the NewConnect markets of the Warsaw Stock Exchange respectively (a total of 902 entities) at the end of 2014. The aim of the study is to classify the companies into the appropriate quotation market on the Warsaw Stock Exchange, i.e. the main market (WSE) or NewConnect (NC). [ABSTRACT FROM AUTHOR]

Documenting which species interact within ecological communities is challenging and labor intensive. As a result, many interactions remain unrecorded, potentially distorting our understanding of network structure and dynamics. We test the utility of four structural models and a new coverage‐deficit model for predicting missing links in both simulated and empirical bipartite networks. We find they can perform well, although the predictive power of structural models varies with the underlying network structure. The accuracy of predictions can be improved by ensembling multiple models. Augmenting observed networks with most‐likely missing links improves estimates of qualitative network metrics. Tools to identify likely missing links can be simple to implement, allowing the prioritization of research effort and more robust assessment of network properties. [ABSTRACT FROM AUTHOR]

Network analysis has been successfully applied in software engineering to understand structural effects in the software. System software is represented as a network graph, and network metrics are used to analyse system quality. This study is motivated by a previous study, which represents the software structure as three‐node subgraphs and empirically identifies that software structure continuously evolves over system releases. Here, the authors extend the previous study to analyse the relation of structural evolution and the defectiveness of subgraphs in the software network graph. This study investigates the behaviour of subgraph defects through software evolution and their impact on system defectiveness. Statistical methods were used to study subgraph defectiveness across versions of the systems and across subgraph types. The authors conclude that software versions have similar behaviours in terms of average subgraph type defectiveness and subgraph frequency distributions. However, different subgraph types have different defectiveness distributions. Based on these conclusions, the authors motivate the use of subgraph‐based software representation in defect predictions and software modelling. These promising findings contribute to the further development of the software engineering discipline and help software developers and quality management in terms of better modelling and focusing their testing efforts within the code structure represented by subgraphs. [ABSTRACT FROM AUTHOR]

Many plant traits might explain the different ecological and network roles of fruit-eating birds. We assessed the relationship of plant productivity, fruit traits (colour, seed size and nutritional quality) and dietary specialization, with the network roles of fruit-eating birds (number of partners, centrality and selectivity) in the Atlantic Forest, Brazil. We classified bird species according to their dietary specialization into three categories: obligate, partial and opportunistic fruit-eating birds. To test if network roles changed according to dietary specialization, fruit productivity and traits, we used a generalized linear model analysis. The selected 14 species of plant interacted with 52 bird species, which consumed 2199 fruits. The most central and generalist fruit-eating bird, Turdus albicolis , interacted with plants that produced more fruits, such as Miconia cinerascens , and had, on average, larger seeds, such as Myrcia splendens. The most selective birds interacted with fruits with a higher concentration of lipids and less intense colour, and plants that produced fewer fruits. Obligate fruit-eating birds, such as Patagioenas plumbea , were more selective than partial and opportunistic birds. Different plant traits are therefore related to the different network roles of fruit-eating birds in the Atlantic Forest, which are also dependent on bird dietary specialization. [ABSTRACT FROM AUTHOR]

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists' abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network's structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks. [ABSTRACT FROM AUTHOR]

Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved. This paper is to explore reliable and robust construction methods of functional brain network using different coupling methods and binarization approaches, based on high-density 128-channel resting state EEG recordings from 16 MDD patients and 16 normal controls (NC). It was found that the combination of imaginary part of coherence and cluster-span threshold outperformed other methods. Based on this combination, right hemisphere function deficiency, symmetry breaking and randomized network structure were found in MDD, which confirmed that MDD had aberrant cognitive processing. Furthermore, clustering coefficient in left central region in theta band and node betweenness centrality in right temporal region in alpha band were significantly negatively correlated with depressive level. And these network metrics had the ability to discriminate MDD from NC, which indicated that these network metrics might be served as the electrophysiological characteristics for probable MDD identification. Hence, this paper may provide reliable methods to construct functional brain network and offer potential biomarkers in MDD. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data’s provenance as represented using the World Wide Web Consortium’s domain-agnostic PROV data model. Specifically, the approach proposes a number of network metrics for provenance data and applies established machine learning techniques over such metrics to build predictive models for some key properties of data. Applying this method to the provenance of real-world data from three different applications, we show that it can successfully identify the owners of provenance documents, assess the quality of crowdsourced data, and identify instructions from chat messages in an alternate-reality game with high levels of accuracy. By so doing, we demonstrate the different ways the proposed provenance network metrics can be used in analysing data, providing the foundation for provenance-based data analytics. [ABSTRACT FROM AUTHOR]

Network science is an emerging field. The purpose of this study is to investigate soccer attacks by using network science. In this study, by applying network science approach, four Turkish National Football Team's attacks analyzed with an open-source NodeXL program. We have focused on two types of attacks: the attacks that end with goals and the ones that don't. Our main aim is to see whether there is a difference between the network metrics of these two types of attacks? Using network metrics, for attacks in a same match we couldn't find important differences but we have found real differences for networks' metrics when opponent team changes. Our findings also support that micro measures can be used for new line-up's. First of all, it should be mentioned that our study is a case study and the results of this study should not be generalized. However, our findings can be the start point for further researches with larger samples sizes. With the help of network science approach, the most effective players could be found, the most compatible line-up for the future games could be chosen and the opponent team's key players could be analyzed. [ABSTRACT FROM AUTHOR]

Network analysis is gaining increasing importance in conservation planning. However, which network metrics are the best predictors of metapopulation persistence is still unresolved. Here, we identify a critical limitation of graph theory-derived network metrics that have been proposed for this purpose: their omission of node self-connections. We resolve this by presenting modifications of existing network metrics, and developing entirely new metrics, that account for node self-connections. Then, we illustrate the performance of these new and modified metrics with an age-structured metapopulation model for a real-world marine reserve network case study, and we evaluate the robustness of our findings by systematically varying particular features of that network. Our new and modified metrics predict metapopulation persistence much better than existing metrics do, even when self-connections are weak. Existing metrics become good predictors of persistence only when self-connections are entirely absent, an unrealistic scenario in the overwhelming majority of metapopulation applications. Our study provides a set of novel tools that can substantially enhance the extent to which network metrics can be employed to understand, and manage for, metapopulation persistence. [ABSTRACT FROM AUTHOR]

There has been an intense focus on the response of species to environmental changes, and more recently, the interactions of species have been examined in a similar way in order to understand the stability of entire communities and networks of interacting species. As a consequence, ecological networks have been placed in spatial and temporal contexts in order to reveal what may drive network variability. Understanding the spatial and temporal variability of ecological networks, and in particular the underlying forces facilitating changes, seems pertinent in our attempts to understand and anticipate how ecological networks may vary and respond to future environmental scenarios., Network variability has been studied at widely differing temporal and spatial scales. For example, studies exploring temporal variability ranges from within-season comparisons to comparisons over vast geological time spans, and the spatial extent ranges from the scale of a single pond to global analyses. Here, we highlight the outcomes from such studies and emphasize the identified mechanisms driving spatio-temporal variability in ecological networks. Specifically, we describe how ecological networks vary over different temporal (years, centuries and millennia) and spatial (local, regional and global) scales, discuss how this variability is monitored and identify potential future directions., Present knowledge allows some tentative generalizations. First, ecological networks tend to exhibit considerable spatial and temporal stability in several macroscopic features (e.g. connectance, nestedness), but studies also show that macroscopic features may change, for example, in relation to mass extinction or steep environmental gradients. Secondly, microscopic features (e.g. individual specialization levels, species roles and partner affiliations), albeit less studied, seem to show strong variability, and in several cases, microscopic instability co-occurs with macroscopic stability. We therefore recommend a stronger focus on this macro-micro interplay and list ideas (e.g. temporal species centrality measures and interaction phenologies), towards expanding the microscopic toolbox of network ecologists., A is available for this article. [ABSTRACT FROM AUTHOR]

Background: Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. LASSO and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs. We propose DEGREECOX, a method that applies network-based regularizers to infer Cox proportional hazard models, when the features are genes and the outcome is patient survival. In particular, we propose to use network centrality measures to constrain the model in terms of significant genes. Results: We applied DEGREECOX to three datasets of ovarian cancer carcinoma and tested several centrality measures such as weighted degree, betweenness and closeness centrality. The a priori network information was retrieved from Gene Co-Expression Networks and Gene Functional Maps. When compared with RIDGE and LASSO, DEGREECOX shows an improvement in the classification of high and low risk patients in a par with NET-COX. The use of network information is especially relevant with datasets that are not easily separated. In terms of RMSE and C-index, DEGREECOX gives results that are similar to those of the best performing methods, in a few cases slightly better. Conclusions: Network-based regularization seems a promising framework to deal with the dimensionality problem. The centrality metrics proposed can be easily expanded to accommodate other topological properties of different biological networks. [ABSTRACT FROM AUTHOR]

Copyright of Spatial Economic Analysis is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states. [ABSTRACT FROM AUTHOR]

1. Virtually all empirical ecological interaction networks to some extent suffer from undersampling. However, how limitations imposed by sampling incompleteness affect our understanding of ecological networks is still poorly explored, which may hinder further advances in the field. 2. Here, we use a plant-hummingbird network with unprecedented sampling effort (2716 h of focal observations) from the Atlantic Rainforest in Brazil, to investigate how sampling effort affects the description of network structure (i.e. widely used network metrics) and the relative importance of distinct processes (i.e. species abundances vs. traits) in determining the frequency of pairwise interactions. 3. By dividing the network into time slices representing a gradient of sampling effort, we show that quantitative metrics, such as interaction evenness, specialization (H2 0), weighted nestedness (wNODF) and modularity (Q; QuanBiMo algorithm) were less biased by sampling incompleteness than binary metrics. Furthermore, the significance of some network metrics changed along the sampling effort gradient. Nevertheless, the higher importance of traits in structuring the network was apparent even with small sampling effort. 4. Our results (i) warn against using very poorly sampled networks as this may bias our understanding of networks, both their patterns and structuring processes, (ii) encourage the use of quantitative metrics little influenced by sampling when performing spatio-temporal comparisons and (iii) indicate that in networks strongly constrained by species traits, such as plant-hummingbird networks, even small sampling is sufficient to detect their relative importance for the frequencies of interactions. Finally, we argue that similar effects of sampling are expected for other highly specialized subnetworks. [ABSTRACT FROM AUTHOR]

The Earth Mover’s Distance (EMD) is a well-known distance metric for data represented as probability distributions over a predefined feature space. Supporting EMD-based similarity search has attracted intensive research effort. Despite the plethora of literature, most existing solutions are optimized for $L^p$ feature spaces (e.g., Euclidean space); while in a spectrum of applications, the relationships between features are better captured using networks. In this paper, we study the problem of answering $k$ -nearest neighbor ( $k$ -NN) queries under network-based EMD metrics (NEMD). We propose Oasis, a new access method which leverages the network structure of feature space and enables efficient NEMD-based similarity search. Specifically, Oasis employs three novel techniques: (i) Range Oracle, a scalable model to estimate the range of $k$ -th nearest neighbor under NEMD, (ii) Boundary Index, a structure that efficiently fetches candidates within given range, and (iii) Network Compression Hierarchy, an incremental filtering mechanism that effectively prunes false positive candidates to save unnecessary computation. Through extensive experiments using both synthetic and real data sets, we confirmed that Oasis significantly outperforms the state-of-the-art methods in query processing cost. [ABSTRACT FROM PUBLISHER]

Promoting tropical forest sustainability among corporate players is a major challenge. Many tools have been developed, but without much success. Southeast Asia has become a laboratory of globalization processes, where the development and success of agribusiness transnationals raises questions about their commitment to environmental concerns. An abundance of literature discusses what determines the behavior of Asian corporations, with a particular emphasis on cultural factors. Our hypothesis is that financial factors, such as ownership structure, may also have a fundamental role. We analyzed the audited accounts of four major Asian agribusiness transnationals. Using network analysis, we deciphered how the 931 companies relate to each other and determine the behavior of the transnationals to which they belong. We compared various metrics with the environmental commitment of these transnationals. We found that ownership structures reflect differences in flexibility, control and transaction costs, but not in ethnicities. Capital and its control, ownership structure, and flexibility explain 97% of the environmental behavior. It means that existing market-based tools to promote environmental sustainability do not engage transnationals at the scale where most of their behavior is determined. For the first time, the inner mechanisms of corporate governance are unraveled in agricultural and forest sustainability. New implications such as the convergence of environmental sustainability with family business sustainability emerged. [ABSTRACT FROM AUTHOR]

The stability and dynamics of multispecies interactions often rely on a small core of species. We examine whether the Yellow-striped Brush-Finch (Atlapetes citrinellus), the only species of bird endemic to the Argentinean Yungas, is a core species for seed dispersal. Of 30 species of fleshy fruit consumed, 16 were dispersed through endozoochory. The Yellow-striped Brush-Finch mostly used the 'cut or mash' method of handling fruit, in which reasonably large seeds (>0.04 g) were discarded or swallowed equally. Medium-sized and small seeds were more often swallowed than discarded. Although the fruits consumed were mostly understorey species, there was no difference in the consumption of fruit from forest understorey or canopy when total fruit abundance in vertical strata was considered. By using interaction network metrics, we determined that the role of Yellow-striped Brush-Finches in seed dispersal during the rainy season was more important at higher altitudes and in the southern sector of its distribution. Our findings support the idea that the Yellow-striped Brush-Finch must be considered a core seed-disperser of understorey and canopy fruits. Use of network metrics is an effective way to assess the importance of individual species in a network, allowing restoration and conservation efforts to be focussed on environments in which these species occur. [ABSTRACT FROM AUTHOR]

An increase in species richness with decreasing latitude is a prominent pattern in nature. However, it remains unclear whether there are corresponding latitudinal gradients in the properties of ecological interaction networks. We investigated the structure of 216 quantitative antagonistic networks comprising insect hosts and their parasitoids, drawn from 28 studies from the High Arctic to the tropics. Key metrics of network structure were strongly affected by the size of the interaction matrix (i.e. the total number of interactions documented between individuals) and by the taxonomic diversity of the host taxa involved. After controlling for these sampling effects, quantitative networks showed no consistent structural patterns across latitude and host guilds, suggesting that there may be basic rules for how sets of antagonists interact with resource species. Furthermore, the strong association between network size and structure implies that many apparent spatial and temporal variations in network structure may prove to be artefacts. [ABSTRACT FROM AUTHOR]

Specific collective behavior forms (for example, mass protests) emerge only if certain conditions are met simultaneously: good structural conduciveness of the group, a pre-existing structural strain, a formed generalized belief, the appearance of precipitating factors, a grass-roots or top-down mobilization for the action and the already-formed perception that the social control instruments are no longer in the authorities' hands. Mass protests seem to follow this 'perfect-storm' recipe, from Tunis' violent protests that kick-started the 'Arab Spring' to the late 2011 riots in London. This paper presents and discusses how structural conduciveness could be measured using network metrics such as k-cores, clustering, cliqueness, reciprocity, cohesion, homophily, structural holes, triad closure. [ABSTRACT FROM AUTHOR]

Researchers have often explained order of progression of group members during joint movement in terms of the influence of ecological pressures but rarely that of social constraints. We studied the order of joining by group members to a movement in semifree-ranging macaques with contrasting social systems: 1 group of Tonkean macaques ( Macaca tonkeana) and 1 group of rhesus macaques ( M. mulatta). We used network metrics to understand roles and associations among individuals. The way the macaques joined a movement reflected the social differences between the species in terms of dominance and kinship. Old and dominant male rhesus macaques were more often at the front of the movement, contrary to the Tonkean macaques, which exhibited no specific order. Moreover, rhesus macaques preferred to join high-ranking or related individuals, whereas Tonkean macaques based associations during joining mostly on sexual relationships with a subgroup of peripheral males. [ABSTRACT FROM AUTHOR]

With increasing global demand for food, international food trade is playing a critical role in balancing the food supply and demand across different regions. Here, using trade datasets of four crops that provide more than 50% of the calories consumed globally, we constructed four international crop trade networks (iCTNs). We observed the increasing globalization in the international crop trade and different trade patterns in different iCTNs. The distributions of node degrees deviate from power laws, and the distributions of link weights follow power laws. We also found that the in-degree is positively correlated with the out-degree, but negatively correlated with the clustering coefficient. This indicates that the numbers of trade partners affect the tendency of economies to form clusters. In addition, each iCTN exhibits a unique topology which is different from the whole food network studied by many researchers. Our analysis on the microstructural characteristics of different iCTNs provides highly valuable insights into distinctive features of specific crop trades and has potential implications for model construction and food security. [ABSTRACT FROM AUTHOR]

Environmental quality (e.g., diversity of resource availability, nesting sites, environmental display) plays an important role in an animal's life. While homogeneous environments can restrict organisms from developing activities such as food seeking (behavioral impairment), more complex environments allow animals to perform activities with learning and behavioral perfecting outcomes. Pesticides are known to affect the learning and foraging behaviors of bees; however, little is known about the counterbalance displayed by the environment. Herein, we conducted two experiments that simulated distinct environmental displays, in which the effects of a fungicide (IndarTM 5EW-febunconazole) on solitary bee foraging activities were tested. We found that the fungicide only impaired the activities of bees in one of the studied environments. The difference in visitation rates and flower exploitation of bees between the two different environmental displays led to changes in metrics of bee–flower networks across environments. Linkage density, a metric associated with pollination efficiency that is known to be impacted by different environments, differed across environments. Our results showed that ecological interaction network metrics can differ regarding the different environmental displays. This study indicates that environmental complexity helps balance the negative effects of pesticides on solitary bees and highlights the potential use of solitary bees as model organisms for experimental simulations of environmental change. [ABSTRACT FROM AUTHOR]

Saura (2018) claims that studies using the Probability of Connectivity metric (PC) had already demonstrated the importance of including node self-connections in network metrics. As originally defined and used, PC cannot test the importance of self-connections. However, with key terms redefined, PC could be a useful tool in future work. [ABSTRACT FROM AUTHOR]

Abstract: Zamborain‐Mason et al. (Ecol. Lett., 20, 2017, 815–831) state that they have newly proposed network metrics that account for node self‐connections. Network metrics incorporating node self‐connections, also referred to as intranode (intrapatch) connectivity, were however already proposed before and have been widely used in a variety of conservation planning applications. [ABSTRACT FROM AUTHOR]