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We present a method for learning spectrally descriptive edge weights for graphs. We generalize a previously known distance measure on graphs (graph diffusion distance [GDD]), thereby allowing it to be tuned to minimize an arbitrary loss function. Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss. We apply this method to discriminate between graphs constructed from shoot apical meristem images of two genotypes of Arabidopsis thaliana specimens: wild-type and trm678 triple mutants with cell division phenotype. Training edge weights and kernel parameters with contrastive loss produce a learned distance metric with large margins between these graph categories. We demonstrate this by showing improved performance of a simple k -nearest-neighbour classifier on the learned distance matrix. We also demonstrate a further application of this method to biological image analysis. Once trained, we use our model to compute the distance between the biological graphs and a set of graphs output by a cell division simulator. Comparing simulated cell division graphs to biological ones allows us to identify simulation parameter regimes which characterize mutant versus wild-type Arabidopsis cells. We find that trm678 mutant cells are characterized by increased randomness of division planes and decreased ability to avoid previous vertices between cell walls.

Knowledge maps have been widely used in knowledge elicitation and representation to evaluate and guide students' learning. To effectively evaluate maps, instructors must select the most informative map features that capture students' knowledge constructs. However, there is currently no clear and consistent criteria to select such features, as empirical studies continue to reflect the (implicit) preferences of scholars. This is challenging for instructors, who may thus ignore critical aspects of a map and/or waste their efforts by examining highly correlated features. To address the research gap of selecting informative graph metrics to assess knowledge maps, this study adopts the machine learning technique of Unsupervised Feature Selection (UFS). Specifically, we extract 12 features used in the prior literature on map assessment (e.g., density, diameter) and use 8 UFS algorithms to rank their importance. By using 202 maps originating from four case studies, we identify features that are generally (un)informative and observe nuances due to context (e.g., learning task, participant profiles). Results suggest that features commonly reported (e.g., number of edges) may not be as informative as less commonly examined aspects (e.g., average degree). Differences exist between maps: the diameter is valuable when learners produce maps from detailed studies, but less informative when maps are directly elicited from the learners' perspectives. The 8 UFS algorithms show five distinct ways to rank features in maps, hence future works may elicit the preferences of instructors for grading and map these preferences to an algorithmic approach (i.e., UFS) that produces a desired ranking. [ABSTRACT FROM AUTHOR]

Internet autonomous system (AS) graph can gain more insight of how the Internet infrastructure is evolving. To construct this AS, the relations between ASes have to be harvested. Subsequently, graph theory has to be leveraged to study the characteristics of the constructed graph. One of the main properties of the AS graph is the type of relations between the ASes and their classification. In this work, AS graph has been constructed from two data sources; locking glass servers and PeeringDB. Subsequently, Gephi has been utilized to extract the characteristic of the constructed graph. Finally, the extracted characteristics have been fed into a K-mean model that clusters the ASes in the graph into three tiers according to the relation’s inferred. Our results show that K-mean can infer the tier of each AS with an accuracy of 88%. Moreover, our results have shown that the Eigen value graph metric can be utilized as the clustering features without other features. [ABSTRACT FROM AUTHOR]

Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed 'node-metric coupling' (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research. [ABSTRACT FROM AUTHOR]

Interpretability fosters trust when humans and artificial intelligence (AI) systems interact, and its value for neural networks in particular cannot be overstated. In this paper, we analyse the emergence and value of neural modularity as it relates to interpretability. We compare the modularity evolved through connectivity constraints in terms of network Q-scores and examine the interpretable qualities of the resultant networks with functional subset regression. The connectivity constraints compared here include those proposed by previous research as well as several novel variations formulated to express neuron input competition and connections per neuron variance. Networks were evolved using HyperNEAT on a free-form substrate. The results indicate that the connection costs successfully promote the evolution of neural modularity across a variety of tasks and show that the novel connection cost variations are competitive with previously explored connection costs. The interpretability assessment shows that while the evolved networks' interpretable qualities are task dependent, two of the compared connection costs deliver statistically different functional module overlap distributions. However, recovered subnetwork module accuracies remain low, highlighting the key points for future research. [ABSTRACT FROM AUTHOR]

Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed ‘node-metric coupling’ (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer’s disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research.

Reliability investigation of measures is important in studies of brain science and neuroengineering. Measures’ reliability hasn’t been investigated across brain states, leaving unknown how reliable the measures are in the context of the change from alert state to fatigue state during driving. To compensate for the lack, we performed a comprehensive investigation. A two-session experiment with an interval of approximately one week was designed to evaluate the reliability of the measures at both sensor and source levels. The results showed that the average intraclass correlation coefficients (ICCs) of the measures at the sensor level were generally higher than those at the source level, except for the directed between-region measures. Single-region measures generally exhibited higher average ICCs relative to between-region measures. The exploration of brain network topology showed that nodal metrics displayed highly varying ICCs across regions and global metrics varied associated with nodal metrics. Single-region measures displayed higher ICCs in the frontal and occipital regions while the between-region measures exhibited higher ICCs in the area involving frontal, central and occipital regions. This study provides an appraisal for the measures’ reliability over a long interval, which is informative for measure selection in practical mental monitoring.

Intraclonal diversification (ID) within the immunoglobulin (IG) genes expressed by B cell clones arises due to ongoing somatic hypermutation (SHM) in a context of continuous interactions with antigen(s). Defining the nature and order of appearance of SHMs in the IG genes can assist in improved understanding of the ID process, shedding light into the ontogeny and evolution of B cell clones in health and disease. Such endeavor is empowered thanks to the introduction of high-throughput sequencing in the study of IG gene repertoires. However, few existing tools allow the identification, quantification and characterization of SHMs related to ID, all of which have limitations in their analysis, highlighting the need for developing a purpose-built tool for the comprehensive analysis of the ID process. In this work, we present the immunoglobulin intraclonal diversification analysis (IgIDivA) tool, a novel methodology for the in-depth qualitative and quantitative analysis of the ID process from high-throughput sequencing data. IgIDivA identifies and characterizes SHMs that occur within the variable domain of the rearranged IG genes and studies in detail the connections between identified SHMs, establishing mutational pathways. Moreover, it combines established and new graph-based metrics for the objective determination of ID level, combined with statistical analysis for the comparison of ID level features for different groups of samples. Of importance, IgIDivA also provides detailed visualizations of ID through the generation of purpose-built graph networks. Beyond the method design, IgIDivA has been also implemented as an R Shiny web application. IgIDivA is freely available at https://bio.tools/igidiva [ABSTRACT FROM AUTHOR]

Reliability investigation of measures is important in studies of brain science and neuroengineering. Measures’ reliability hasn’t been investigated across brain states, leaving unknown how reliable the measures are in the context of the change from alert state to fatigue state during driving. To compensate for the lack, we performed a comprehensive investigation. A two-session experiment with an interval of approximately one week was designed to evaluate the reliability of the measures at both sensor and source levels. The results showed that the average intraclass correlation coefficients (ICCs) of the measures at the sensor level were generally higher than those at the source level, except for the directed between-region measures. Single-region measures generally exhibited higher average ICCs relative to between-region measures. The exploration of brain network topology showed that nodal metrics displayed highly varying ICCs across regions and global metrics varied associated with nodal metrics. Single-region measures displayed higher ICCs in the frontal and occipital regions while the between-region measures exhibited higher ICCs in the area involving frontal, central and occipital regions. This study provides an appraisal for the measures’ reliability over a long interval, which is informative for measure selection in practical mental monitoring. [ABSTRACT FROM AUTHOR]

River networks are frequently simulated for use in the development and testing of ecological theory. Currently, two main algorithms are used, stochastic branching networks (SBNs) and optimal channel networks (OCNs). The topology of these simulated networks and 'real' rivers is often quantified using graph theoretic metrics; however, to date, there has not been a comprehensive analysis of how these algorithms compare regarding graph theoretic metrics, or an analysis of metric redundancy and variability across dendritic ecological networks. We aim to provide guidance as to which algorithm and metrics should be used, and under what circumstances.We performed an extensive simulation study in which we (a) identified orthogonal sets of metrics that describe the topology of real and simulated river networks, (b) analysed the relationship between algorithm hyper‐parameters and node topology metrics, (c) determined whether simulated and real rivers are indistinguishable in their graph metric scores and (d) examined how patterns of species abundances compare across the three network types.We identified two orthogonal sets of node metrics; those that describe centrality and those that describe neighbourhood characteristics. Both stochastic branching networks and optimal channel networks can reproduce network topology metric scores of real rivers, but this relationship is dependent on the algorithm hyper‐parameters used. Finally, using a metapopulation model, we show that both SBNs and OCNs can reproduce ecological patterns of species abundances similar to those of real rivers.SBNs and OCNs can replicate the node topology of real rivers. The choice of which algorithm to use will depend on the research aims, SBNs are faster to generate and more tractable, whereas OCNs can reproduce a wider variety of the characteristics of real rivers, but are more time‐consuming to generate. When quantifying node topology in river networks, we recommend the orthogonal node metrics eccentricity, when interested in network centrality, and mean neighbour degree, when interested in local node importance. [ABSTRACT FROM AUTHOR]

The study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P

COVID-19 pandemic enforced students in schools and universities all around the world to study using the online and blinded learning. In these learning models, students depend on the Internet for information searching of different scientific essentials to improve their skills and to overcome the gap of facing instructors. One of the most popular sources of information is Wikipedia. In this work, we attempt to study the relations of different math essential pages of Wikipedia to find the relation between these topics. A graph has been constructed for these pages. The graph theoretical metrics, such as, centrality, edge weights and clustering coefficient have been extracted of the constructed graph. The extracted values have been investigated to gain more insights of the math topics that should be studied first. The extracted results show that the in-degree property of the articles and the betweenness value of these articles are correlated. Moreover, there is no relation between the in /out-degree of the pages. Finally, the constructed graph has a small average shortest path and a high global cluster coefficient. This proves that the constructed graph follows the small world phenomenon. [ABSTRACT FROM AUTHOR]

This article introduces a new class of models for multiple networks. The core idea is to parameterize a distribution on labeled graphs in terms of a Fréchet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter that controls the concentration of this distribution about its mean. Entropy is the natural parameter for such control, varying from a point mass concentrated on the Fréchet mean itself to a uniform distribution over all graphs on a given vertex set. We provide a hierarchical Bayesian approach for exploiting this construction, along with straightforward strategies for sampling from the resultant posterior distribution. We conclude by demonstrating the efficacy of our approach via simulation studies and two multiple-network data analysis examples: one drawn from systems biology and the other from neuroscience. This article has online . [ABSTRACT FROM AUTHOR]

In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios. [ABSTRACT FROM AUTHOR]

The Euclidian metric gives better results when organizing a multi-agent interaction environment. The analytical basis for this metric is the matrix inverse to a simple matrix description of an undirected graph. In many applied tasks, the usual matrix inversion and real numbers in the framework of finite bit-depth calculations are quite sufficient. However, the unweighted undirected graph is a discrete object, and traditional metrics are able to support processing in the field of rational numbers. Here we show that the Euclidian metric has the same property. Moreover, the space with the dot product is much richer in possibilities in comparison to the spaces where only norms are introduced. Here these possibilities are at the heart of a simple algorithm for calculating the rational entries of the required inverse matrix. Also in the Euclidian space one can use the most important relationship between its elements - orthogonality. The results of numerical experiments are presented. [ABSTRACT FROM AUTHOR]

Previous researches have produced a number of conclusions on the functional magnetic resonance imaging (fMRI) study for autism spectrum disorder (ASD) patients, but there are different opinions about the brain regions of the lesions. In order to study ASD more deeply, an advanced framework, i.e., genetic-evolutionary random support vector machine (SVM) cluster, was proposed in this paper. In our method, an initial cluster of multiple SVMs was first built by randomly picking samples and features. Then, these SVMs were selected to recombine and mutate the aim of genetic evolution until the number of genetic evolution which reached the threshold or the classification accuracy was stable. We evaluated the proposed method by using the resting state fMRI data (103 ASD patients and 106 healthy controls), which achieved a 96.8% accuracy. Based on the classification results, the abnormal brain regions were found out. This study suggests the pathogenesis of ASD to a certain extent and offers great assistance for the diagnosis of potential patients with ASD.

In recent years, communication networks on modern power substations have grown both in size and complexity, demanding the highest levels of reliability. However, there is no unique criterion to define the structure of the topology in such networks, since in every substation the end user implements their own topology or the topology suggested by a vendor, according to IEC 61850 standard guidelines. This paper proposes a methodology, using integer linear programming, to solve the problem of generating a reliable network topology in a software-defined power substations context. The trustworthiness of the reached solution is evaluated using terminal reliability techniques, graph metrics, and end-to-end time delay performance. The obtained results confirm that the proposed network topology is highly reliable to be implemented in power substations, according to the network redundancy considerations proposed by the IEC 62439 standard, and the operation time requirements suggested by the on IEC 61850 standard. In addition, we present software defined networking-based solutions for loop-based topologies in the proposed network topology, which would be technically unfeasible using traditional network protocols. These solutions include algorithms to solve problems related to the broadcast traffic containment and the diffusion and reliability of the multicast traffic.

Wireless Sensor Networks (WSN) has proliferated in the past decade. These networks consist of massive number of battery-powered nodes distrusted over a given area. The nodes are responsible for sensing the environment and delivering the sensed data to a central point, named sink node. In order to reduce the power consumption of these nodes, sleeping/waking scheduling strategy has been proposed. In this work, a new hybrid sleeping/waking scheduling algorithm is proposed based on graph theory metrics and unsupervised K-mean machine learning algorithm. In the proposed algorithm, the sink node is responsible for calculating the metrics and clustering the nodes into three main clusters; dense, mid and light. Subsequently, the algorithm attempts to reduce the load on the nodes in light cluster in order to prolong the network lifetime. The algorithm has been simulated in 3D WSN with a clustering routing protocol. The simulation results show that the algorithm reduces the number of working sensor network nodes without affecting the network diameter. Moreover, the scheduling strategy has prolonged the network lifetime and has reduced the number of disconnected components. [ABSTRACT FROM AUTHOR]

Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress.Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics.Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23–74.17%), 66.05% (95% CI = 63.06–69.04%), and 80.35% (95% CI = 77.77–82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28–73.12%), 86.75% (95% CI = 84.48–88.92%), and 55.65% (95% CI = 52.61–58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance.Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

Agenesis of the corpus callosum (AgCC) is a congenital brain malformation characterized by the complete or partial failure to develop the corpus callosum. Despite missing the largest white matter bundle connecting the left and right hemispheres of the brain, studies have shown preserved inter-hemispheric communication in individuals with AgCC. It is likely that plasticity provides mechanisms for the brain to adjust in the context of AgCC, as the malformation disrupts programmed developmental brain processes very early on. A proposed candidate for neuroplastic response in individuals with AgCC is strengthening of intra-hemispheric structural connections. In the present study, we explore this hypothesis using a graph-based approach of the structural connectome, which enables intra- and inter-hemispheric analyses at multiple resolutions and quantification of structural characteristics through graph metrics. Structural graph metrics of 19 children with AgCC (13 with complete, 6 with partial AgCC) were compared to those of 29 typically developing controls (TDC). Associations between structural graph metrics and a wide range of neurobehavioral outcomes were examined using a multivariate data-driven approach (Partial Least Squares Correlation, PLSC). Our results provide new evidence suggesting structural strengthening of intra-hemispheric pathways as a neuroplastic response in the acallosal brain, and highlight regional variability in structural connectivity in children with AgCC compared to TDC. There was little evidence that structural graph properties in children with AgCC were associated with neurobehavioral outcomes. To our knowledge, this is the first report leveraging graph theory tools to explicitly characterize whole-brain intra- and inter-hemispheric structural connectivity in AgCC, opening avenues for future research on neuroplastic responses in AgCC.

Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress. Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics. Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23–74.17%), 66.05% (95% CI = 63.06–69.04%), and 80.35% (95% CI = 77.77–82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28–73.12%), 86.75% (95% CI = 84.48–88.92%), and 55.65% (95% CI = 52.61–58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance. Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA. [ABSTRACT FROM AUTHOR]

Unique and outstanding physiochemical properties of zeolite materials make them extremely useful in a variety of applications which offer a great potential for the development of material science. Recent studies of heavy element doping and insertion into these zeolites have opened up novel applications to tungsten-based biofuels, luminescence spectra and so on. Although a recent study by Prabhu et al. [Mater. Res. Exp. 7, 055006 (2020)] presents several distance-based and bond additive topological indices of Linde type A zeolites, we show here that their results are erroneous. We also present novel techniques for zeolite materials impregnated or doped with very heavy elements such as W, Au, Pt, Bi to obtain topological indices that include relativistic parameters derived from relativistic quantum computations. We have derived exact mathematical expressions for the various topological indices with relativistic parameters and yet in a simple three dimensional symmetrical formula based on the structural descriptions of Linde type A zeolites with very heavy atoms. We obtain closed formula for nine types Mostar indices to quantify peripherality measures of zeolite structures. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score ( p = 0.003) and negatively with FSIQ ( p = 0.04) and processing speed index ( p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical ( p = 0.001) and frontal ( p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.

In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time. [ABSTRACT FROM AUTHOR]

Due to the widespread popularity of cyber-social networks, research on community and social role discovery has drawn immense attention. Most of the available literature consider community and social role detection as areas of a disjoint problem domain. Moreover, existing research on concurrent community and social role detection utilizes a predefined set of roles and lacks in providing enough ground truth information for the proof. Hence, there is a high need for community structure analysis from an independent social perspective. Therefore, we propose a community and social role detection method called SocioRank* using email interaction data crawled from a graduate class of students. SocioRank* detects the underlying communities and ranks individuals on the basis of degree, closeness, and betweenness centrality. We perform extensive graph metrics-based experiments to evaluate the effectiveness of the proposed method. The comparison of results with the ground truth information demonstrates the high accuracy of SocioRank* method in community and role discovery. [ABSTRACT FROM AUTHOR]

Highlights • Higher clustering and longer path length in brain-injured patients. • Graph metrics can be used to diagnose and differentiate TBI patients. • TBI patients show a shift away from balanced small-world structure. • We reveal a pattern of change to be used to guide hypothesis-driven research. Abstract Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (g ö=ö1.445, CI=[0.512, 2.378], p ö=ö0.002) and longer characteristic path length (g ö=ö0.514, CI=[0.190, 0.838], p ö=ö0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers. [ABSTRACT FROM AUTHOR]

The concept of p-modulus gives a way to measure the richness of a family of objects on a graph. In this paper, we investigate the families of connecting walks between two fixed nodes and show how to use p-modulus to form a parametrized family of graph metrics that generalize several well-known and widely-used metrics. We also investigate a characteristic of metrics called the \antisnowaking exponent" and present some numerical findings supporting a conjecture about the new metrics. We end with explicit computations of the new metrics on some selected graphs. [ABSTRACT FROM AUTHOR]

This article is about an algorithm of educational ontology’s development based on intermediate models – mind maps and concept maps. It is offered to use different metrics such as subjective metrics and graph topology’s metrics for automated optimization of mind maps.