Changes In Network Structure Research Articles

Modeling Complex Networks Based on Deep Reinforcement Learning

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

Ecological network complexity scales with area.

Larger geographical areas contain more species-an observation raised to a law in ecology. Less explored is whether biodiversity changes are accompanied by a modification of interaction networks. We use data from 32 spatial interaction networks from different ecosystems to analyse how network structure changes with area. We find that basic community structure descriptors (number of species, links and links per species) increase with area following a power law. Yet, the distribution of links per species varies little with area, indicating that the fundamental organization of interactions within networks is conserved. Our null model analyses suggest that the spatial scaling of network structure is determined by factors beyond species richness and the number of links. We demonstrate that biodiversity-area relationships can be extended from species counts to higher levels of network complexity. Therefore, the consequences of anthropogenic habitat destruction may extend from species loss to wider simplification of natural communities.

The impact of structural changes of trade dependence network on cobalt price from the perspective of industrial chain

The impact of structural changes of trade dependence network on cobalt price from the perspective of industrial chain

The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics

Multiple sclerosis is a neuroinflammatory and neurodegenerative disorder of the central nervous system that can be considered a network disorder. In MS, lesional pathology continuously disconnects structural pathways in the brain, forming a disconnection syndrome. Complex functional network changes then occur that are poorly understood but closely follow clinical status. Studying these structural and functional network changes has been and remains crucial to further decipher complex symptoms like cognitive impairment and physical disability. Recent insights especially implicate the importance of monitoring network hubs in MS, like the thalamus and default-mode network which seem especially hit hard. Such network insights in MS have led to the hypothesis that as the network continues to become disconnected and dysfunctional, exceeding a certain threshold of network efficiency loss leads to a “network collapse”. After this collapse, crucial network hubs become rigid and overloaded, and at the same time a faster neurodegeneration and accelerated clinical (and cognitive) progression can be seen. As network neuroscience has evolved, the MS field can now move towards a clearer classification of the network collapse itself and specific milestone events leading up to it. Such an updated network-focused conceptual framework of MS could directly impact clinical decision making as well as the design of network-tailored rehabilitation strategies. This review therefore provides an overview of recent network concepts that have enhanced our understanding of clinical progression in MS, especially focusing on cognition, as well as new concepts that will likely move the field forward in the near future.

Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow

Understanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. Crude oil is the most imported energy in the world. Investigating the crude oil maritime network status and predicting the crude oil traffic flow changes has great significance for the global trade, especially for key crude oil importing/exporting regions and countries. To address this, a system-based approach using long short-term memory and graph convolution network for the crude oil traffic flow forecasting named LGCOTFF is introduced. The LGCOTFF approach constructs a maritime transportation network firstly, and then calculates and predicts the node traffic flow based on trajectory data and crude oil berth geographical position. Firstly, we construct a maritime crude oil transportation network based on supply-demand relationship, ship trajectory and route information. Then, we design an approach to calculate how many crude oil ships finished up-load/offtake tasks in a single week for each port, and gather this data to countries and regions. Finally, we design a deep learning neural network named long short-term memory and graph convolution network (L-GCN) to extract the temporal and spatial characteristics of crude oil transportation, and predict the node traffic flow. We evaluate the proposed model on China, Russia, Middle East and America respectively and observe consistent improvement of more than 10% over state-of-the-art baselines.

Insights into the Relationship between Cross-Linking Network Structure and Phase Change Performances Toward Multifunctional Epoxy/Bio-Based Wax Form-Stable Phase Change Materials

Insights into the Relationship between Cross-Linking Network Structure and Phase Change Performances Toward Multifunctional Epoxy/Bio-Based Wax Form-Stable Phase Change Materials

Hyperspectral Image Restoration With Self-Supervised Learning: A Two-Stage Training Approach

Hyperspectral image (HSI) denoising is a crucial preprocessing task to improve the performance of the subsequent HSI interpretation and applications. With recent progress in deep learning, HSI denoising methods based on deep neural networks have attracted increasing interest and achieved the state-of-the-art performance. Nevertheless, most of these methods are based on network structures originally developed for grayscale and color images and require a change of network structure to be applicable to HSIs. The new network architectures often lead to complicated models and limited flexibility, which, in turn, result in difficulty in learning and demand of a large number of training samples. In this article, we propose an innovative two-stage learning method including pretraining and fine-tuning procedures. In the first stage, a denoising convolutional neural network can be pretrained with pairs of corrupted and clean images. In the second stage, the pretrained network is fine-tuned via a self-supervised learning strategy to capture the spectral correlation in HSIs. The training pairs in the second stage are constructed from the neighboring band images in the target noisy HSI, leading to a novel idea of embedding spectral information into denoiser through the target image, rather than the change of the network architecture. This model has strong adaptability such that many image denoising networks can be easily adopted for HSIs, while the external hyperspectral training set is optional but not mandatory. Experimental results show that our method has competitive performance compared with the state-of-the-art approaches.

Macroscopic dynamics of gene regulatory networks revealed by individual entropy

Complex systems are usually high-dimensional with intricate interactions among internal components, and may display complicated dynamics under different conditions. While it is difficult to measure detailed dynamics of each component, proper macroscopic description of a complex system is crucial for quantitative studies. In biological systems, each cell is a complex system containing a huge number of molecular components that are interconnected with each other through intricate molecular interaction networks. Here, we consider gene regulatory networks in a cell, and introduce individual entropy as a macroscopic variable to quantify the transcriptional dynamics in response to changes in random perturbations and/or network structures. The proposed individual entropy measures the information entropy of a system at each instant with respect to a basal reference state, and may provide temporal dynamics to characterize switches of system states. Individual entropy provides a method to quantify the stationary macroscopic dynamics of a gene set that is dependent on the gene regulation connections, and can be served as an indicator for the evolution of network structure variation. Moreover, the individual entropy with reference to a preceding state enables us to characterize different dynamic patterns generated from varying network structures. Our results show that the proposed individual entropy can be a valuable macroscopic variable of complex systems in characterizing the transition processes from order to disorder dynamics, and to identify the critical events during the transition process.

Nucleation mechanisms in a SiO2-Li2O-P2O5-ZrO2 biomedical glass-ceramic: Insights on crystallisation, residual glasses and Zr4+ structural environment

In manufacturing reliable glass-ceramics, bulk chemistry and appropriate thermal treatments are critical points. To better understand the influence of nucleation parameters on the crystallisation behaviour and mechanical properties of multicomponent glass-ceramics, we studied the effects of time and temperature on phase stabilisation, the evolution of the crystal phases and physical properties of a Zr-rich lithium-silicate having a SiO2/Li2O molar ratio < 2. Here, we also provide insights into the structural environment of Zr4+ at the early stage of nucleation by Anomalous X-ray Scattering (AXS). By reverse-engineering a commercial material through remelting and sequential thermal treatments, we observed the strong effect of the nucleation temperature on the final mineral assemblage and of the volume fractions of the lithium-disilicate phase on the fracture toughness (KIc). Indeed, the KIc value was increased from ∼0.8 MPa.m½ in the pristine glass up to ∼ 2 MPa.m½, if compared to the original commercial material exhibiting a KIc value of 1.6 MPa.m½. Furthermore, the synthesis of residual glasses at different stages of the thermal treatments allowed us to observe the changes in the glass network structure and properties. AXS was used to extract the contribution exclusively coming from Zr for different nucleation steps. The differential pair distribution functions ΔZrg(r) show two distinct peaks at ∼2.10 Å and 3.44 Å, respectively associated with Zr-O and Zr-Zr/Zr-Si correlations with the formation of edge sharing in addition to corner sharing polyhedra. It was discerned the occurrence of (sub)nano-sized clusters enriched in Zr. These local clusters do not show changes during the different thermal treatments, either for higher temperatures or longer times. No Zr phases are stabilised in the crystallised materials, suggesting that these clusters do not evolve above the critical nucleus size. All evidence points to a highly heterogeneous environment and the coexistence of three structural regions i) (sub)nano Zr-clusters, ii) P enriched μ-areas, iii) residual silicate glass. The evolution and spatial distribution of these units influence the final properties.

Altered Default Mode Network Is Associated With Cognitive Impairment in CADASIL as Revealed by Multimodal Neuroimaging.

Background and Purpose: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy caused by mutations in the NOTCH3 gene is a hereditary cerebral small vessel disease, manifesting with stroke, cognitive impairment, and mood disturbances. Functional or structural changes in the default mode network (DMN), which plays important role in cognitive and mental maintenance, have been found in several neurological and mental diseases. However, it remains unclear whether DMN is altered in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL).Methods: Multimodal imaging methods, including MRI and positron emission tomography (PET), were applied to evaluate the functional, structural, and metabolic characteristics of DMN in 25 patients with CADASIL and 42 healthy controls.Results: Compared with controls, patients with CADASIL had decreased nodal efficiency and degree centrality of the dorsal medial pre-frontal cortex and hippocampal formation within DMN. Structural MRI and diffusion tensor imaging (DTI) showed decreased gray matter volume and fiber tracks presented in the bilateral hippocampal formation. Meanwhile, PET imaging showed decreased metabolism within the whole DMN in CADASIL. Furthermore, correlation analyses showed that these nodal characteristics, gray matter volume, and metabolic signals of DMN were related to cognitive scores in CADASIL.Conclusions: Our results suggested that altered network characteristics of DMN might play important roles in cognitive deficits of CADASIL.

Psychopathological networks: Theory, methods and practice

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Transmission line adaptive protection scheme: A new fault detection approach based on pilot superimposed impedance

High impedance faults, changing network structure and line series compensation can lead to overreaching or underreacting of the conventional distance relays which can imperial security, stability, and sustainability of power systems. To overcome these challenging issues, this paper presents a new adaptive primary protection scheme for transmission lines based on pilot superimposed impedance (PSI). In the proposed scheme, PSI is defined as the ratio of the superimposed component of voltage phasor obtained from the local end to the sum of superimposed component current phasors obtained from both ends of the protected transmission line. Since the proposed PSI shows the different responses for internal and external faults, it is compared with the developed threshold based on Thevenin impedance for detecting the faulted phases inside and outside the protected line. In the case of internal faults, the PSI is less than the network Thevenin impedance which is seen from the bus adjacent to the relay. Due to establishing the proposed threshold based on the Thevenin impedance seen from the bus behind the corresponding relay, it can be adaptively updated when the network structure is changed. This feature helps to yield robust protection not only in different contingencies of the network but also in the case of weak source networks. The proposed method is also tested in the case of high fault resistance, load encroachment (LE) condition and series capacitive compensation in the long-distance transmission line. The obtained results confirm the security and dependability of the proposed protection scheme.

Network analysis of the structure and change in the mini‐mental state examination: A nationally representative sample

AbstractBackgroundThe structure of the Mini‐Mental state examination (MMSE) is inconsistent across factor analytic studies, and yet to be examined based on network analysis. The current study aims to identify the (I) cross‐sectional network structure and (II) longitudinal network changes of the MMSE.MethodThe MMSE was administered to a nationally representative sample of older adults (age 50 and over) in Ireland twice over 4 years (2012–2013: N = 7207; 2016: N = 5715). Psychometric network analysis was computed at each time point to identify the structure, strength and magnitude of the network associations. Item clustering was examined, and modularity scores were computed to measure the overall strength of clustering. Centrality indices were used to identify the main aspects of the MMSE. Longitudinal differences between the networks were examined.ResultCross‐sectionally, the MMSE network structure clustered into a single community with orientation items identified as most central. Longitudinally, the MMSE was time invariant regarding structure, centrality and magnitude of the positive associations between the items. The average magnitude of the negative associations increased over time [(t(65.15) = 3.78, p &lt; 0.001; time 1: M = − 0.59, SD = 0.58 time 2: M = − 1.65, SD = 1.97] as did their percentage.ConclusionNetwork analysis of the MMSE showed that the measure consisted of a single entity of cognitive functioning irrespective of time. Orientation items were repeatedly identified as most central. Longitudinal changes of the network were evident in increased negative associations between selected cognitive components after 4 years of follow‐up. These changes may be explained by neuro‐cognitive compensation processes.

Interactive Anomaly Detection in Dynamic Communication Networks

Network flows are the basic components of the Internet. Considering the serious consequences of abnormal flows, it is crucial to provide timely anomaly detection in dynamic communication networks. To obtain accurate anomaly detection results in dynamic networks, supervision from experts is highly demanded. However, to obtain high-quality ground truth of abnormal flows, we suffer from two major problems: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">limited labor resources</i> : experts with the latest domain knowledge are much fewer than the large number of flows; and (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic environment</i> : considering the new abnormal patterns (i.e., new attacks) and continuously changing network structures, it requires timely supervision to adaptively update the parameters. To tackle these problems, we propose HADDN, a novel bandit framework for periodic-updated anomaly detection in dynamic communication networks. We formulate the task as a bandit problem, where by interactions, supervision is offered by human experts to provide the ground truth to a fraction of flows. We construct semi-parametric expected rewards to optimize the estimation of flows’ abnormality in limited interactions. Also, we utilize feature-based clusters and structural correlations to make connections between historical flows and new flows to improve both efficiency and accuracy of abnormality estimation. What’s more, we provide two implementations for the semi-parametric expected reward of the proposed HADDN with theoretical proof. Experimental evaluations on public datasets demonstrate the substantial improvement of our proposed approaches compared to state-of-art anomaly detection methods.

A new automated tool to quantify nucleoid distribution within mitochondrial networks

Mitochondrial DNA (mtDNA) maintenance is essential to sustain a functionally healthy population of mitochondria within cells. Proper mtDNA replication and distribution within mitochondrial networks are essential to maintain mitochondrial homeostasis. However, the fundamental basis of mtDNA segregation and distribution within mitochondrial networks is still unclear. To address these questions, we developed an algorithm, Mitomate tracker to unravel the global distribution of nucleoids within mitochondria. Using this tool, we decipher the semi-regular spacing of nucleoids across mitochondrial networks. Furthermore, we show that mitochondrial fission actively regulates mtDNA distribution by controlling the distribution of nucleoids within mitochondrial networks. Specifically, we found that primary cells bearing disease-associated mutations in the fission proteins DRP1 and MYH14 show altered nucleoid distribution, and acute enrichment of enlarged nucleoids near the nucleus. Further analysis suggests that the altered nucleoid distribution observed in the fission mutants is the result of both changes in network structure and nucleoid density. Thus, our study provides novel insights into the role of mitochondria fission in nucleoid distribution and the understanding of diseases caused by fission defects.

Análisis de herramientas que permitan el modelado de tráfico en redes SD

El presente trabajo consiste en el estudio de herramientas que permiten configurar un entorno de simulación o emulación de redes dentro de un mismo sistema facilitando de cierta forma plantear un escenario de red que cuenta con topologías y flujos de tráfico. Específicamente se enfocó en el modelado de tráfico en redes SDN (Redes Definidas por Software) que permite que un solo equipo administre una red en su totalidad, haciéndolo capaz de tolerar cambios en toda la estructura de la red sin necesidad de detener los servicios. Se realiza una investigación bibliográfica de fuentes de carácter documental sobre simuladores que permiten el modelado de redes para determinar la más adecuada; los instrumentos analizadas fueron: NS-3, EstiNet y MiniNet. Posteriormente de acuerdo a la información obtenida y el estudio del&#x0D; mismo, se realizó una tabla comparativa con los parámetros que distinguen a cada una, permitiendo de esta forma concluir de manera idónea la mejor opción para emulación de topologías en ambientes SDN.

Abstract 9301: The Association of Pulse Pressure With Brain Structure and Function in Healthy Individuals

Introduction: Hypertension is associated to changes of structural and functional brain networks that lead to impairment of cognitive performance. Recently, pulse pressure (PP) has been suggested as cardiovascular risk factor in hypertension. Hypothesis: In this study, we tested whether elevated PP is accompanied by brain structural and functional changes in healthy individuals without hypertension. Methods: We investigated 115 healthy subjects (41±17 years, 55% females) without cardiovascular, neurological or psychiatric disorders. Structural and functional brain imaging was performed on a 3 Tesla Siemens Prisma. We assessed cognitive function using the MATRICS Consensus Cognitive Battery. Participants were categorized by PP in normal and high PP (PP&gt;50 mmHg) according to resting blood pressure measurements in the laboratory. The normal PP group was reduced to match the high PP group with respect to age, gender, and education level. From resting functional brain images, we extracted whole-brain network connectivity between 260 independent brain regions to estimate global graph-theoretical features for integration (efficiency), resilience (assortativity), and segregation (clustering coefficient). Cortical thickness and volume were extracted from structural images using the Freesurfer image analysis suite. Results: While subgroups did not differ in terms of heart rate variability, baroreflex sensitivity was decreased in the high PP group. Furthermore, subjects with high PP performed worse in the trail-making task that primarily assesses processing speed (p&lt;0.001, FDR-corr.). Graph theoretical analyses revealed that functional brain networks in subjects with high PP were less efficient, clustered and resilient than normal PP (all p&lt;0.001, FDR-corr.). There were no differences in brain structure between groups. However, we found a negative correlation of PP with cortical thickness in the lateral prefrontal cortex over all subjects, suggesting that high PP is associated with cortical thinning in this area (corrected for age, gender and education). Conclusion: Elevated PP is associated with an altered organization of functional brain networks, lower cortical thickness and decreased processing speed in healthy subjects.

A BUSINESS MODEL ENABLING A PASSENGER-DISTANCE-IMPROVED LONG-HAUL NETWORK TO DECREASE TRANSPORT INEFFICIENCIES

The air transport system faces pressing challenges arising from CO2-emission reduction targets, fragmented passenger needs, and a highly competitive market environment. Aiming for a reduction of CO2-emissions in the long-haul sector, a holistic solution is suggested incorporating both operational and technological innovations in three areas: (1) changes in the air transport network structure, (2) (liquid) hydrogen as energy carrier, and (3) new aircraft and cabin design. To these ends, this paper focuses on the implications resulting from a passenger-distance improved air transport network. Demand pooling is proposed to enable higher load factors, offer more point-to-point connections for passengers, and generate new revenue sources for airlines. Based on a discussion of traditional airline business models, a seat exchange platform named “ShAirline” is proposed, allowing multiple providers to rent out cabin space. The underlying business model is evaluated considering implications for aircraft and cabin design, new passenger services, additional revenue opportunities, passenger journey times, required aircraft, as well as implications for airports. Findings indicate that the proposed seat exchange platform in conjunction with a change in aircraft ownership structure assist in removing inefficiencies across the current long-haul network and help offset barriers connected to the use of novel eco-efficient technologies.

Whole-brain white matter network reorganization in HIV.

The human immunodeficiency virus (HIV) causes an infectious disease with a high viral tropism toward CD4 T-lymphocytes and macrophage. Since the advent of combined antiretroviral therapy (CART), the number of opportunistic infectious disease has diminished, turning HIV into a chronic condition. Nevertheless, HIV-infected patients suffer from several life-long symptoms, including the HIV-associated neurocognitive disorder (HAND), whose biological substrates remain unclear. HAND includes a range of cognitive impairments which have a huge impact on daily patient life. The aim of this study was to examine putative structural brain network changes in HIV-infected patient to test whether diffusion-imaging-related biomarkers could be used to discover and characterize subtle neurological alterations in HIV infection. To this end, we employed multi-shell, multi-tissue constrained spherical deconvolution in conjunction with probabilistic tractography and graph-theoretical analyses. We found several statistically significant effects in both local (right postcentral gyrus, right precuneus, right inferior parietal lobule, right transverse temporal gyrus, right inferior temporal gyrus, right putamen and right pallidum) and global graph-theoretical measures (global clustering coefficient, global efficiency and transitivity). Our study highlights a global and local reorganization of the structural connectome which support the possible application of graph theory to detect subtle alteration of brain regions in HIV patients.Clinical Relevance-Brain measures able to detect subtle alteration in HIV patients could also be used in e.g. evaluating therapeutic responses, hence empowering clinical trials.

Quantifying changes in the British cattle movement network

The modelling of disease spread is crucial to the farming industry and policy makers. In some of these industries, excellent data exist on animal movements, along with the networks that these movements create, and allow researchers to model spread of disease (both epidemic and endemic). The Cattle Tracing System is an online recording system for cattle births, deaths and between-herd movements in the United Kingdom and is an excellent resource for any researchers interested in networks or modelling infectious disease spread through the UK cattle system. Data exist that cover many years, and it can be useful to know how much change is occurring in a network, to help judge the merit of using historical data within a modelling context. This article uses the data to construct weighted directed monthly movement networks for two distinct periods of time, 2004–2006 and 2015–2017, to quantify by how much the underlying structure of the network has changed. Substantial changes in network structure may influence policy-makers directly or may influence models built upon the network data, and these in turn could impact policy-makers and their assessment of risk. We examined 13 network metrics, ranging from general descriptive metrics such as total number of nodes with movements and total movements, through to metrics to describe the network (e.g., Giant weakly and strongly connected components) and metrics calculated per node (betweenness, degree and strength). Mixed effect models show that there is a statistically significant effect of the period (2004–2006 vs 2015–2017) in the values of nine of the 13 network metrics. For example median total degree decreased by 19%. In addition to examining networks for two time periods, two updates of the data were examined to determine by how much the movement data stored for 2004–2006 had been cleansed between updates. Examination of these updates shows that there are small decreases in problem movements (such as animals leaving slaughterhouses) and therefore evidence of historical data being improved between updates. In combination with the significant effect of period on many of the network metrics, the modification of data between updates provides further evidence that the most recent available data should be used for network modelling. This will ensure that the most representative descriptions of the network are available to provide accurate modelling results to best inform policy makers.