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Abstract 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 $$\sim 85\%$$ ∼ 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.

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]

Clustering analysis of small watersheds is an effective tool for identifying the similarity of runoff generation and concentration. In this paper, 545 small watersheds in the hilly areas of Shandong Province were investigated, and 12 indicators representing their climate and subsurface characteristics were selected to identify communities based on hydrological similarity. We further analyzed the hydrological connections among the small watersheds within each community using three indicators (network mean, centrality, and k-core). Finally, the clustering results were evaluated on the basis of the small watershed flood peak modulus. The results of this complex network method indicate that the study area contained six large communities and nine small communities. The community-clustering results were reasonable and showed the interconnectedness of the watersheds within each community. The three network indicators adequately described the degree of similarity, the representativeness of the watersheds, and the spatial scales of similar hydrological features. This method should be helpful for addressing the issue of parameter transplantation in ungauged watersheds and implementation of a flood risk management strategy. HIGHLIGHTS Small watershed classification is conducted in a complex network approach.; Network evaluation metrics were used to represent hydrological linkages between small watersheds in the community.; The flood modulus of the small watersheds was calculated using the inference equation method for reasonableness verification.;

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]

Abstract 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.

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]

The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics. The aim of this study is to systematically inventory and classify recent studies that have investigated IoT metrics. The authors conducted a literature review based on studies published between January 2010 and December 2021 using a set of five research questions (RQs) on the current knowledge bases for IoT metrics. A total of 158 IoT metrics were identified and classified into 12 categories according to the different parts and aspects of an IoT system. To cover the overall performance of an IoT system, the 12 categories were organized into an ontology. The results show that the category of network metrics was most frequently discussed in 43% of the studies and, with the highest number of metrics at 37%. This study can provide guidelines for researchers and practitioners in selecting metrics for IoT systems and valuable insights into areas for improvement and optimization.

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.

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]

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The Biodiversity – Ecosystem Functioning (B–EF) relationship remains a topic of ongoing debate with most studies focusing on primary productivity, and documenting that this relationship takes many forms. It remains unclear if biodiversity drives productivity or productivity shapes biodiversity or the relationship is bidirectional. B-EF studies explore almost exclusively the relationship between species richness and ecosystem functioning, while the role of biotic interactions, a key component of ecosystem functioning, has been neglected. Here, using data of 80 local plant–pollinator networks on 20 Aegean islands, and of gross primary productivity (GPP) from the MODIS satellite, we explored the bidirectional relationship between interaction network structure (nestedness and specialization), species richness (plants and pollinators) and mean and inter-annual variability of GPP. We found that nestedness and specialisation of plant–pollinator networks is driven by mean GPP. However, specialisation alone was a significant predictor of mean GPP, implying that networks tend to be more specialised in low-productivity areas. Pollinator species richness exerted a strong effect on mean GPP with the remaining factors playing a minor role, while the effect of mean GPP on pollinator species richness was weaker. Furthermore, the nestedness of plant–pollinator networks drives inter-annual variability of GPP with more nested networks displaying less variability, which is in accordance with the predictions of the insurance hypothesis. Plant and pollinator species richness were also associated with inter-annual variability of GPP.

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]

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.

An animal neural system ranges from a cluster of a few neurons to a brain of billions. At the lower range, it is possible to test each neuron for its role across a set of environmental conditions. However, the higher range requires another approach. One method is to disentangle the organization of the neuronal network. In the case of the entorhinal cortex in a rodent, a set of neuronal cells involved in spatial location activate in a regular grid-like arrangement. Therefore, it is of interest to develop methods to find these kinds of patterns in a neural network. For this study, a square grid arrangement of neurons is quantified by network metrics and then applied for identification of square grid structure in areas of the fruit fly brain. The results show several regions with contiguous clusters of square grid arrangements in the neural network, supportive of specialization in the information processing of the system.

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]

The Biodiversity – Ecosystem Functioning (B–EF) relationship remains a topic of ongoing debate with most studies focusing on primary productivity, and documenting that this relationship takes many forms. It remains unclear if biodiversity drives productivity or productivity shapes biodiversity or the relationship is bidirectional. B-EF studies explore almost exclusively the relationship between species richness and ecosystem functioning, while the role of biotic interactions, a key component of ecosystem functioning, has been neglected. Here, using data of 80 local plant–pollinator networks on 20 Aegean islands, and of gross primary productivity (GPP) from the MODIS satellite, we explored the bidirectional relationship between interaction network structure (nestedness and specialization), species richness (plants and pollinators) and mean and inter-annual variability of GPP. We found that nestedness and specialisation of plant–pollinator networks is driven by mean GPP. However, specialisation alone was a significant predictor of mean GPP, implying that networks tend to be more specialised in low-productivity areas. Pollinator species richness exerted a strong effect on mean GPP with the remaining factors playing a minor role, while the effect of mean GPP on pollinator species richness was weaker. Furthermore, the nestedness of plant–pollinator networks drives inter-annual variability of GPP with more nested networks displaying less variability, which is in accordance with the predictions of the insurance hypothesis. Plant and pollinator species richness were also associated with inter-annual variability of GPP. [Display omitted] [ABSTRACT FROM AUTHOR]

INTRODUCTION: Early adverse life events (EALs) increase the risk for chronic medical and psychiatric disorders by altering early neurodevelopment. The aim of this study was to examine associations between EALs and network properties of core brain regions in the emotion regulation and salience networks, and to test the influence of sex on these associations. METHODS: Resting-state functional and diffusion tensor magnetic resonance imaging were obtained in healthy individuals (61 men, 63 women). Functional and anatomical network properties of centrality and segregation were calculated for the core regions of the two networks using graph theory. Moderator analyses were applied to test hypotheses. RESULTS: The type of adversity experienced influences brain wiring differently, as higher general EALs were associated with decreased functional and anatomical centrality in salience and emotion regulation regions, while physical and emotional EALs were associated with increased anatomical centrality and segregation in emotion regulation regions. Sex moderated the associations between EALs and measures of centrality; with decreased centrality of salience and emotion regulation regions with increased general EALs in females, and increased centrality in salience regions with higher physical and emotional EALs in males. Increased segregation of salience regions was associated with increased general EALs in males. Centrality of the amygdala was associated with physical symptoms, and segregation of salience regions was correlated with higher somatization in men only. CONCLUSIONS: Emotion regulation and salience regions are susceptible to topological brain restructuring associated with EALs. The male and female brains appear to be differently affected by specific types of EALs.

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]

Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented.

In recent decades, within the boundaries of complexity sciences, network science has been used to analyze many kinds of networks. A complex system is formed by smaller subsystems which can be designed independently yet function together as a whole. Modules of a network can be called as groups, clusters or communities and modularity can be defined as a measure of the structure of networks or graphs. If it has dense connections within a network’s modules and sparse connections between nodes in different modules, in this case networks have high modularity. At the end of each football match, successful pass networks can be achieved. These modular structures can be thought as “independent yet function as a whole” football modules. Generally so called modules in the technical directors’ thoughts can be listed as defense, midfielders area and strikers area. If they want to know how modules are generated and how their disconnection leads to functional decay they should analyze the modularity formed as a result of a football match. The aim of this study is to examine to what extent the football teams' managers have implemented their strategies in games. First, 10 matches with the e-analysis football program were analyzed. And then modularity analysis began with transforming the video of a football match into a pass network. Using this pass network, network metrics was to be computed and then these metrics was to be used to make a modularity analysis using Gephi. After modularity analysis using Gephi, modularity classes of these networks were found for all networks. When the results obtained from the modularity analysis were examined, it was observed that the number of modules varied between 2 and 4. Consequently, it was observed that the systems found as a result of the modularity analysis were very different than the planned systems.

To meet the increasing demand for food around the world, pesticides are widely used and will continue to be widely used in agricultural production to reduce yield losses and maintain product quality. International pesticide trade serves to reallocate the distribution of pesticides around the world. We investigate the statistical properties of the international trade networks of five categories of pesticides from the view angle of temporal directed and weighted networks. We observed an overall increasing trend in network size, network density, average in- and out-degrees, average in- and out-strengths, temporal similarity, and link reciprocity, indicating that the rising globalization of pesticides trade is driving the networks denser. However, the distributions of link weights remain unchanged along time for the five categories of pesticides. In addition, all the networks are disassortatively mixed because large importers or exporters are more likely to trade with small exporters or importers. We also observed positive correlations between in-degree and out-degree, in-strength and out-strength, link reciprocity and in-degree, out-degree, in-strength, and out-strength, while node’s local clustering coefficient is negatively related to in-degree, out-degree, in-strength, and out-strength. We show that some structural and dynamic properties of the international pesticide trade networks are different from those of the international trade networks, highlighting the presence of idiosyncratic features of different goods and products in the international trade.

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]

Complex network metrics are used for ranking the importance of nodes in many different applications, e.g., spreader identification and percolation analysis. Examples for such node metrics include the degree centrality and betweenness centrality. The computation of some of these metrics is computationally expensive, e.g. the computation of betweenness takes O(N3) of computation steps in the worst case, where N is the number of nodes. In this study, we investigate the ability of deep learning via graph embedding to predict node centrality metrics in complex networks. Our study reveals that deep learning can identify vital nodes well for several of these metrics. Compared to exact computation with prohibitive costs, deep learning can get significant speedups, which scale up well with the size of the network. Further investigation on betweenness centrality reveals that the accuracy of deep learning is not as good as some tailor-made, tuned approximation algorithms. However, deep learning offers a nice trade-off between runtime and quality with its linear computational complexity regarding the network size, which makes it scalable on large networks and especially for these metrics with expensive computational costs. Our work is the first to explore the ability of deep learning on a wide range of networks metrics, and will hopefully induce future work on improved graph embeddings, tuned for specific network metrics.

Abstract Social structures and interpersonal relationships may be represented as social networks consisting of nodes corresponding to people and links between pairs of nodes corresponding to relationships between those people. Social networks can be constructed by examining actual groups of people and identifying the relationships of interest between them. However, there are circumstances where such empirical social networks are unavailable or their use would be undesirable. Consequently, methods to generate synthetic social networks that are not identical to real-world networks but have desired structural similarities to them have been developed. A process for generating synthetic social networks based on assigning human personality types to the nodes and then adding links between nodes based on the compatibility of the nodes’ personalities was developed. Two new algorithms, Probability Search and Compatibility-Degree Matching, for finding an effective assignment of personality types to the nodes were developed, implemented, and tested. The two algorithms were evaluated in terms of realism, i.e., the similarity of the generated synthetic social to exemplar real-world social networks, for 14 different real-world social networks using 20 standard quantitative network metrics. Both search algorithms produced networks that were, on average, more realistic than a standard network generation algorithm that does not use personality, the Configuration Model. The algorithms were also evaluated in terms of computational complexity.

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]

In advance of the meeting, 34 researchers prepared position statements on the following kinds of questions:• What is the current state of knowledge with respect to spatiotemporal constraints on social networks and informationflows, particularly from a meta-network perspective?• How can theories of social network interaction be extended to incorporate the constraining effects of space, time, theInternet, and mass media?• Can probability distributions be developed for networks or network metrics that are parameterized by spatial andtemporal separation?• What rich sources of data can be found to calibrate and parameterize these new models?• What new metrics and models can be developed for assessing critical nodes, groups, and trails in and throughnetworks that take spatio-temporal constraints, the Internet, and mass-media effects into account?• Can we develop novel methods for visualizing the operation of spatio-temporal constraints and their effects on the flow of ideas and information through meta-networks?• What methods of inference are appropriate for detection of spatio-temporal and network constraints in crowd-sourced data, and what are appropriate metrics of uncertainty?

In order to evaluate the restoration from a functional approach, we compared avian seed dispersal interactions between a restored forest and a reference site. For this purpose, abundance and species richness of frugivorous birds and ornitochours plants were recorded. Then, interaction matrices were generated based on fruit consumption and the morphological overlap between fruit and bill size. Our results show that the number of links per species and nestedness, were higher in the reference forest, while the number of interactions varied greatly in the restored forest. Furthermore, the most connected species in the two forests was the Masked Flowerpierce (Diglossa cyanea). We discussed that although some interactions were reestablished, there are still some differences between sites and hence, management actions such as enrichment with ornithochorous plants, in order to enhance interactions and to improve the seed bank should be promoted. [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]

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]

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]

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]

Social network analysis can be defined as the method of examining social structures with the help of networks and graph theory.In this paper, we aim to develop a model which can replicate the features of social networks present in the real world, and can easily derivethe various characteristics associated with those networks. We define a model based on Random Recursive Tree (RRT) which is an extension to the Barabási-Albert model, capturing key features, such as the degree variance, the network diameter, the average path length, degree distribution etc. with respect to the size of the network and analyze the trends between these metrics. We then validate the proposed model by constructing various cascade trees with the help of a real-life social network, WeChat data-set. [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.

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.

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]

When using diffusion MRI streamlines tractograms to construct structural connectomes, ideally, each streamline should connect exactly 2 regions-of-interest (i.e. network nodes) as defined by a given brain parcellation scheme. However, the ill-posed nature of termination criteria in many tractography algorithms can cause streamlines apparently being associated with zero, one, or more than two grey matter (GM) nodes; streamlines that terminate in white matter or cerebrospinal fluid may even end up being assigned to nodes if the definitions of these nodes are not strictly constrained to genuine GM areas, resulting in a misleading connectome in non-trivial ways. Based on both in-house MRI data and state-of-the-art data provided by the Human Connectome Project, this study investigates the actual influence of streamline-to-node assignment methods, and their interactions with fibre-tracking terminations and brain parcellations, on the construction of pairwise regional connectivity and subsequent connectomic measures. Our results show that the frequency of generating successful pairwise connectivity is heavily affected by the convoluted interactions between the applied strategies for connectome construction, and that minor changes in the mechanism can cause significant variations in the within- and between-module connectivity strengths as well as in the commonly-used graph theory metrics. Our data suggest that these fundamental processes should not be overlooked in structural connectomics research, and that improved data quality is not in itself sufficient to solve the underlying problems associated with assigning streamlines to brain nodes. We demonstrate that the application of advanced fibre-tracking techniques that are designed to correct for inaccuracies of track terminations with respect to anatomical information at the fibre-tracking stage is advantageous to the subsequent connectome construction process, in which pairs of parcellation nodes can be more robustly identified from streamline terminations via a suitable assignment mechanism. • The process of assigning streamlines to brain nodes can be a significant problem. • Using over-dilated node images may result in biologically unrealistic connections. • Connectome topology is significantly affected by connection construction mechanisms. • Improved data quality cannot fix the underlying issues of connectome construction. • Advanced tractography methods can improve the construction of pairwise connections. [ABSTRACT FROM AUTHOR]