Network Graph Theory Research Articles

Multilayer adaptive networks in neuronal processing

The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to understand neurotransmission (fast synaptic transmission) networks. However, neurons use other forms of communication as neuromodulation that, instead of conveying excitation or inhibition, change neuronal and synaptic properties. This additional neuromodulatory layers condition and reconfigure the connectome. In this paper, we propose that multilayer adaptive networks, in which different synaptic and neurochemical layers interact, are the appropriate framework to explain neuronal processing. Then, we describe a simplified multilayer adaptive network model that accounts for these extra-layers of interaction and analyse the emergence of interesting computational capabilities.

Task-specificity in focal dystonia is shaped by aberrant diversity of a functional network kernel.

Task-specific focal dystonia selectively affects the motor control during skilled and highly learned behaviors. Recent data suggest the role of neural network abnormalities in the development of the pathophysiological dystonic cascade. We used resting-state functional MRI and analytic techniques rooted in network science and graph theory to examine the formation of abnormal subnetwork of highly influential brain regions, the functional network kernel, and its influence on aberrant dystonic connectivity specific to affected body region and skilled motor behavior. We found abnormal embedding of sensorimotor cortex and prefrontal thalamus in dystonic network kernel as a hallmark of task-specific focal dystonia. Dependent on the affected body region, aberrant functional specialization of the network kernel included regions of motor control management in focal hand dystonia (writer's cramp, musician's focal hand dystonia) and sensorimotor processing in laryngeal dystonia (spasmodic dysphonia, singer's laryngeal dystonia). Dependent on skilled motor behavior, the network kernel featured altered connectivity between sensory and motor execution circuits in musician's dystonia (musician's focal hand dystonia, singer's laryngeal dystonia) and abnormal integration of sensory feedback into motor planning and executive circuits in non-musician's dystonia (writer's cramp, spasmodic dysphonia). Our study identified specific traits in disorganization of large-scale neural connectivity that underlie the common pathophysiology of task-specific focal dystonia while reflecting distinct symptomatology of its different forms. Identification of specialized regions of information transfer that influence dystonic network activity is an important step for future delineation of targets for neuromodulation as a potential therapeutic option of task-specific focal dystonia. © 2018 International Parkinson and Movement Disorder Society.

Network Models in Neuroimaging: A Survey of Multimodal Applications

Mapping the brain structure and function is one of the hardest problems in science. Different image modalities, in particular the ones based on magnetic resonance imaging (MRI) can shed more light on how it is organised and how its functions unfold, but a theoretical framework is needed. In the last years, using network models and graph theory to represent the brain structure and function has become a major trend in neuroscience. In this review, we outline how network modelling has been used in neuroimaging, clarifying what are the underlying mathematical concepts and the consequent methodological choices. The major findings are then presented for structural, functional and multimodal applications. We conclude outlining what are still the current issues and the perspective for the immediate future.

Weighted Stochastic Block Models of the Human Connectome across the Life Span

The human brain can be described as a complex network of anatomical connections between distinct areas, referred to as the human connectome. Fundamental characteristics of connectome organization can be revealed using the tools of network science and graph theory. Of particular interest is the network’s community structure, commonly identified by modularity maximization, where communities are conceptualized as densely intra-connected and sparsely inter-connected. Here we adopt a generative modeling approach called weighted stochastic block models (WSBM) that can describe a wider range of community structure topologies by explicitly considering patterned interactions between communities. We apply this method to the study of changes in the human connectome that occur across the life span (between 6–85 years old). We find that WSBM communities exhibit greater hemispheric symmetry and are spatially less compact than those derived from modularity maximization. We identify several network blocks that exhibit significant linear and non-linear changes across age, with the most significant changes involving subregions of prefrontal cortex. Overall, we show that the WSBM generative modeling approach can be an effective tool for describing types of community structure in brain networks that go beyond modularity.

Distributed real-time computing and application of chemical pipeline network based on hadoop

The author carries out a study on distributed real-time computing and application of chemical pipeline network based on Hadoop. Firstly, the initial value setting is done by setting the linearized initial value. Secondly, the distributed computing framework Hadoop is used to perform the pipeline network graph theory computing. Then the distributed pipeline network based on B/S is used to build the distributed simulation platform of chemical pipeline network. The result of the simulation can be calculated in unit time. The pipeline network running on this system can achieve the purpose of being real-time and stable and is worthy of promotion and application.

Applying network theory to animal movements to identify properties of landscape space use.

Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node-level (local) metrics and graph-level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node-level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter-patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph-level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large-scale data sets efficiently, providing a framework for conservationists to analyze movement data. Functions created for the analyses are available within the R package moveNT.

Composiciones familiares y configuraciones matrimoniales en redes genealógicas indígenas del oriente jujeño, Siglo XIX

Gregory Bateson propone como objeto y fin de sus reflexiones en Espíritu y Naturaleza la noción de “pauta que conecta”, a través de preguntas tales como ¿cuál es la pauta que conecta a todas las criaturas vivientes? Hemos escogido, como objeto de nuestro análisis, en cambio, una dimensión fractal de aquella propuesta, restringida a los vínculos desarrollados en los campos nucleares de la reproducción sociocultural. Nuestro interés es reconocer y analizar las configuraciones de algunos vínculos de parentesco y la ocupación que hace de una región delimitada espacial y temporalmente un conjunto discreto de familias campesinas indígenas. Esto es, los criterios, regularidades y tendencias que exhiben estos conjuntos en sus aspectos más básicos y primarios: aquellos que estructuran, regulan y distribuyen la reproducción social a través de la organización doméstica y las alianzas matrimoniales. Los materiales escogidos para tal tarea provienen de una genealogía reconstruida de conjuntos parentales indígenas rurales del siglo XIX. Nuestra estrategia analítica se basa en el Análisis de Redes de Parentesco y la Teoría de Grafos.

Analyze the Quality of Power using A Graph theory network Flow Algorithm

This paper explains about the power quality and transmits maximum power between the grid based electrical systems. These system stream calculations are connected to a graphical portrayal of a power framework topology to decide the base number of framework branches should have been lost keeping in mind the end goal to ensure separating the two hubs in the framework that are chosen. The number of framework branches that are discovered fills in as an inexact marker of framework vulnerabilities. The technique utilized as a part of these availability investigations makes utilization of surely understood diagram hypothesis organize stream greatest stream calculations, additionally presents another calculation for refreshing an old system stream answer for the misfortune of just a solitary framework branch. The proposed new calculation permits for essentially diminished arrangement time that is fancied in a ongoing condition. The benefit of utilizing the proposed technique is delineated by utilizing a point by point case of the 2008 island development that happened in the Entergy control framework.

Phenotypic Graphs and Evolution Unfold the Standard Genetic Code as the Optimal.

In this work, we explicitly consider the evolution of the Standard Genetic Code (SGC) by assuming two evolutionary stages, to wit, the primeval RNY code and two intermediate codes in between. We used network theory and graph theory to measure the connectivity of each phenotypic graph. The connectivity values are compared to the values of the codes under different randomization scenarios. An error-correcting optimal code is one in which the algebraic connectivity is minimized. We show that the SGC is optimal in regard to its robustness and error-tolerance when compared to all random codes under different assumptions.

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks.

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts.

Influence of features of the transport network pattern on the haul cycle length between its nodes on the example of the transport network of Ukraine

The object of research is the road network based on the example of the existing network in Ukraine. During the audit of the main parameters affecting the performance of the transport systems, the mileage of vehicles is determined. It is established that road networks stipulate not only the actual distance between its nodes, but also the potential parameters of the characteristics of traffic of vehicles over the network. Disadvantages of road networks are their cost, limited bandwidth and pattern. In the course of the research of the road network pattern, the methods of network analysis and graph theory are used. A matrix of the shortest distances between regional centers of Ukraine is calculated. As a result of the research it is shown that transportation to Kherson, Odesa or Simferopol is carried out on routes that are on average 44 %, 46 % and 64 % more than possible if there is an appropriate road network. It is determined that the quality of the transport network pattern can be described by the straightness coefficient. The definition of the straightness coefficient of the road network is given. Calculations of the values have established that in the considered network this coefficient takes values from 0.24 to 0.64. The investigated road network offers the organization of communication between the cities of Kharkiv and Cherkasy with the straightness coefficient of the road network – 0.6.

Impact of hydroclimatic variability on regional-scale landscape connectivity across a dynamic dryland region

In dynamic dryland regions, accounting for spatiotemporal landscape dynamics is essential to understanding how ecological habitat networks are affected by hydroclimatic variability at regional or sub-continental scales. Here we assess how changes in the distribution and availability of surface water influence potential landscape connectivity for water-dependent organisms by combining graph theory network analysis with a Landsat-derived, seasonally continuous 25-year surface-water time-series. We focused on Australia’s Murray Darling Basin (MDB), a globally significant and ecologically stressed 1millionkm2 semi-arid region recently affected by two unprecedented hydroclimatic extremes: the 1997–2010 Millennium Drought and 2010–2012 La Niña floods. We constructed potential habitat networks for two dispersal abilities using circuit theory resistance distances, and used ‘habitat availability’ graph theory metrics as indicators of regional-scale connectivity. We analysed 792 unique potential habitat networks containing over 6.6 million nodes, making our study one of the largest spatially explicit ecological network analyses yet conducted. Our indicators of connectivity revealed consistently positive but spatially heterogeneous relationships between flooded habitat area and landscape connectivity. Connectivity increased by over two orders of magnitude along the spectrum from severe drought to flood, associated with a transition from connectivity driven by intra-habitat or short-distance dispersal during drought to long-distance dispersal during wet conditions. Reductions in connectivity during drought were lower than expected given equivalent decreases in surface water habitat area, suggesting habitat network structure provides a degree of resistance to dry conditions. By providing insights into the processes driving connectivity during different phases along the drought-flood spectrum, our approach may assist in guiding conservation management aimed at maintaining or improving landscape connectivity within dynamic environments faced with increasing hydroclimatic variability.

Optimal PMU placement solution: graph theory and MCDM‐based approach

In this study, a new method using graph theory and analytical hierarchy process (AHP)-based multi-criteria decision making approach has been proposed to solve optimal phasor measurement unit (PMU) placement (OPP) problem for complete network observability. The proposed approach assures maximum measurement redundancy too. A decision matrix is formed with some unique criteria from the concept of network graph theory and this helps to formulate priority vertex ranking using AHP. Candidate vertex search technique is used to enumerate the candidate PMU placement vertices from priority vertex ranking. The contribution of zero injection buses (ZIBs) in PMU placement problem has been considered. The proposed technique is further analysed for complete observability under single PMU loss or line outage cases. The proposed approach is tested on IEEE 14-bus, 24-bus, 30-bus, 57-bus, 118-bus, and New England 39-bus systems. To verify the computational efficiency and higher redundancy of solutions of the proposed method, the results are compared with some well-established methods reported in the literature. The proposed method is further applied to IEEE 300-bus, Polish 2383-bus and 3120-bus system to show the efficacy of the proposed approach in solving OPP in large scale power systems.

Application of Graph Theory in Transportation Networks

When trying to estimate the maximum number of people who can fly from a city Ci to a city Cj through different routes and airports, one can use a networks N as a model. Let us assume the source s, as the origin Ci of the trip and the sink t, as the destination Cj , and the remaining internal nodes vi are the intermediate airports. An arc a = (vi , vj) of N represents a direct flight from the city vi to the city vj and c(a) denotes the maximum number of seats available on a direct flight from vi to vj .

Contribution of connectivity metrics to the assessment of biodiversity – Some methodological considerations to improve landscape planning

Land use change and the resulting physical and functional disconnection of ecological networks are some of the driving forces of biodiversity loss. Landscape planning and environmental assessments are essential instruments for addressing such problems. Methods for quantifying and predicting the impacts of fragmentation on biodiversity are needed, as are methods for deriving objectives and measures in the landscape planning process. While a number of different methodologies regarding network analysis and graph theory provide tools and methods for analyzing ecological networks, graph theory is a tool that may be helpful for reducing negative ecological impacts and finding appropriate solutions in the landscape planning process. These methods can be used as evaluation tools in the planning process, to analyze and visualize different possible scenarios for the participation process, or to define areas that are most important for measures to preserve or enhance biodiversity. Using the example of three target species in Saxony, the use of the Probability of Connectivity Index (PC) as a functional connectivity index for potential connectivity analysis is examined. Implementation and requirements for the planning process are described.

Network Graph Theory Analysis of Semantic Fluency in Russian - English Bilinguals (P4.205)

Network Graph Theory Analysis of Semantic Fluency in Russian - English Bilinguals (P4.205)

Altered structural connectivity of pain-related brain network in burning mouth syndrome-investigation by graph analysis of probabilistic tractography.

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome featuring idiopathic oral pain and burning discomfort despite clinically normal oral mucosa. The etiology of chronic pain syndrome is unclear, but preliminary neuroimaging research has suggested the alteration of volume, metabolism, blood flow, and diffusion at multiple brain regions. According to the neuromatrix theory of Melzack, pain sense is generated in the brain by the network of multiple pain-related brain regions. Therefore, the alteration of pain-related network is also assumed as an etiology of chronic pain. In this study, we investigated the brain network of BMS brain by using probabilistic tractography and graph analysis. Fourteen BMS patients and 14 age-matched healthy controls underwent 1.5T MRI. Structural connectivity was calculated in 83 anatomically defined regions with probabilistic tractography of 60-axis diffusion tensor imaging and 3D T1-weighted imaging. Graph theory network analysis was used to evaluate the brain network at local and global connectivity. In BMS brain, a significant difference of local brain connectivity was recognized at the bilateral rostral anterior cingulate cortex, right medial orbitofrontal cortex, and left pars orbitalis which belong to the medial pain system; however, no significant difference was recognized at the lateral system including the somatic sensory cortex. A strengthened connection of the anterior cingulate cortex and medial prefrontal cortex with the basal ganglia, thalamus, and brain stem was revealed. Structural brain network analysis revealed the alteration of the medial system of the pain-related brain network in chronic pain syndrome.

Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice.

This paper discusses how social network analyses and graph theory can be implemented in team sports performance analyses to evaluate individual (micro) and collective (macro) performance data, and how to use this information for designing practice tasks. Moreover, we briefly outline possible limitations of social network studies and provide suggestions for future research. Instead of cataloguing discrete events or player actions, it has been argued that researchers need to consider the synergistic interpersonal processes emerging between teammates in competitive performance environments. Theoretical assumptions on team coordination prompted the emergence of innovative, theoretically driven methods for assessing collective team sport behaviours. Here, we contribute to this theoretical and practical debate by re-conceptualising sports teams as complex social networks. From this perspective, players are viewed as network nodes, connected through relevant information variables (e.g. a ball-passing action), sustaining complex patterns of interaction between teammates (e.g. a ball-passing network). Specialised tools and metrics related to graph theory could be applied to evaluate structural and topological properties of interpersonal interactions of teammates, complementing more traditional analysis methods. This innovative methodology moves beyond the use of common notation analysis methods, providing a richer understanding of the complexity of interpersonal interactions sustaining collective team sports performance. The proposed approach provides practical applications for coaches, performance analysts, practitioners and researchers by establishing social network analyses as a useful approach for capturing the emergent properties of interactions between players in sports teams.

Computational study of 'HUB' microRNA in human cardiac diseases.

MicroRNAs (miRNAs) are small non-coding RNAs ~22 nucleotides long that do not encode for proteins but have been reported to influence gene expression in normal and abnormal health conditions. Though a large body of scientific literature on miRNAs exists, their network level profile linking molecules with their corresponding phenotypes, is less explored. Here, we studied a network of 191 human miRNAs reported to play a role in 30 human cardiac diseases. Our aim was to study miRNA network properties like hubness and preferred associations, using data mining, network graph theory and statistical analysis. A total of 16 miRNAs were found to have a disease node connectivity of >5 edges (i.e., they were linked to more than 5 diseases) and were considered hubs in the miRNAcardiac disease network. Alternatively, when diseases were considered as hubs, >10 of miRNAs showed up on each ‘disease hub node’. Of all the miRNAs associated with diseases, 19 miRNAs (19/24= 79.1% of upregulated events) were found to be upregulated in atherosclerosis. The data suggest micro RNAs as early stage biological markers in cardiac conditions with potential towards microRNA based therapeutics.

Multi-criteria robustness analysis of metro networks

Metros (heavy rail transit systems) are integral parts of urban transportation systems. Failures in their operations can have serious impacts on urban mobility, and measuring their robustness is therefore critical. Moreover, as physical networks, metros can be viewed as topological entities, and as such they possess measurable network properties. In this article, by using network science and graph theory, we investigate ten theoretical and four numerical robustness metrics and their performance in quantifying the robustness of 33 metro networks under random failures or targeted attacks. We find that the ten theoretical metrics capture two distinct aspects of robustness of metro networks. First, several metrics place an emphasis on alternative paths. Second, other metrics place an emphasis on the length of the paths. To account for all aspects, we standardize all ten indicators and plot them on radar diagrams to assess the overall robustness for metro networks. Overall, we find that Tokyo and Rome are the most robust networks. Rome benefits from short transferring and Tokyo has a significant number of transfer stations, both in the city center and in the peripheral area of the city, promoting both a higher number of alternative paths and overall relatively short path-lengths.