Static Network Model Research Articles

Reconstruction of dynamic protein-protein interaction network via graph convolutional network

Traditional studies are mostly based on static protein network models. The highly averaged and idealized static protein network does not represent well this dynamic nature that encompasses different time, place and reaction conditions. Therefore, we propose PreDPPI, a method for convolving dynamic protein-protein interaction networks (PINs) using knowledge graph reconstruction of relational graphs, which achieves better performance in terms of dynamics compared to other methods. We found that the longer the length of timestamps used in reconstructing dynamic PINs, the better the prediction. In addition, we identified three hub genes (NUSAP1, SCG3, and CKAP2L) in dynamic PINs that were shown to be significantly associated with glioma prognosis. In conclusion, this work reconstructed a web-based knowledge graph convolving dynamic PINs and identified three hub genes in the GEO glioma dataset that were confirmed to be highly associated with glioma prognosis.

Adaptive payoff-driven interaction in networked snowdrift games

In social dilemmas, most interactions are transient and susceptible to restructuring, leading to continuous changes in social networks over time. Typically, agents assess the rewards of their current interactions and adjust their connections to optimize outcomes. In this paper, we introduce an adaptive network model in the snowdrift game to examine dynamic levels of cooperation and network topology, involving the potential for both the termination of existing connections and the establishment of new ones. In particular, we define the agent’s asymmetric disassociation tendency toward their neighbors, which fundamentally determines the probability of edge dismantlement. The mechanism allows agents to selectively sever and rewire their connections to alternative individuals to refine partnerships. Our findings reveal that adaptive networks are particularly effective in promoting a robust evolution toward states of either pure cooperation or complete defection, especially under conditions of extreme cost-benefit ratios, as compared to static network models. Moreover, the dynamic restructuring of connections and the distribution of network degrees among agents are closely linked to the levels of cooperation in stationary states. Specifically, cooperators tend to seek broader neighborhoods when confronted with the invasion of multiple defectors.

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network.

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

Random graphs and real networks with weak geometric coupling

Geometry can be used to explain many properties commonly observed in real networks. It is therefore often assumed that real networks, especially those with high average local clustering, live in an underlying hidden geometric space. However, it has been shown that finite-size effects can also induce substantial clustering, even when the coupling to this space is weak or nonexistent. In this paper, we study the weakly geometric regime, where clustering is absent in the thermodynamic limit but present in finite systems. Extending Mercator, a network embedding tool based on the popularity×similarity S1/H2 static geometric network model, we show that, even when the coupling to the geometric space is weak, geometric information can be recovered from the connectivity alone for networks of any size. The fact that several real networks are best described in this quasigeometric regime suggests that the transition between nongeometric and geometric networks is not a sharp one. Published by the American Physical Society 2024

A dynamic-static combination risk analysis framework for berthing/unberthing operations of maritime autonomous surface ships considering temporal correlation

The autonomy of the ship is bound to bring new risk challenges to its berthing/unberthing operations process. Potential damages resulting from these risks can range from minor hull deformations to substantial losses of assets and environmental harm. To address these risks in the process of berthing/unberthing operations of MASSs or mitigate them to an acceptable level, this paper aims to propose a risk analysis framework combining dynamic and static Bayesian network models, and which can consider the temporal correlation of the values of risk factors, to help ships avoid the occurrence of operational accidents. In this framework, a set of probabilistic model is included to calculate the real-time risk value of autonomous berthing operation, so as to analyze and solve the time-dependent problem of risk change with time during berthing/unberthing of autonomous ships. In addition, the developed model can determine the key influencing factors and possible key situations during the berthing/unberthing of autonomous ships, which can support the risk prevention and mitigation measures in the process of autonomous ship berthing/unberthing. Finally, the applicability of the proposed risk analysis framework is analyzed through a virtual simulation test case of autonomous berthing/unberthing.

Heterogeneity and dynamics in network models

SummaryWe propose an empirical spatial modeling framework that allows for both heterogeneity and dynamics in economic network connections. We establish the model's stationarity and ergodicity properties and show that the model's implied filter is invertible. While highly flexible, the model is straightforward to estimate by maximum likelihood. We apply the model to three datasets for Eurozone sovereign credit risk over the period Dec‐2009 to Dec‐2022. Accounting for both heterogeneity and time‐variation turns out to be empirically important both in‐sample and out‐of‐sample. The new model uncovers intuitive patterns that would go unnoticed in either homogeneous and/or static spatial financial network models.

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13–18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

Correlation Study of Temporal Climate Networks Based on Weighted Cumulative Node Evaluation

In order to systematically analyze the characteristics of climate change in China in recent years, this paper selects the data of 6 climate factors from China meteorological stations during 1984-2016 for 33 years. With the help of complex network theory, the representative climate nodes in China are determined by node importance evaluation method with the power to accumulate intermediate centrality. Multiple single-factor temporal static and temporal dynamic climate network models are constructed to explore the correlation of representative node networks of Climate in China. The results show that the temperature network has the strongest correlation. The temporal static network has the weakest correlation with precipitation network, while the temporal dynamic network has the weakest correlation with wind velocity network and shows a weak fluctuation downward trend.

Networks with correlated edge processes

Abstract This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependence and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the challenge in modelling and inferring correlation between a large number of variables.

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.

Evolving scattering networks for engineering disorder

Network science provides a powerful tool for unraveling the complexities of social, technological and biological systems. Constructing networks using wave phenomena is also of great interest in devising advanced hardware for machine learning, as shown in optical neural networks. Although most wave-based networks have employed static network models, the impact of evolving models in network science provides strong motivation to apply dynamical network modeling to wave physics. Here the concept of evolving scattering networks for scattering phenomena is developed. The network is defined by links, node degrees and their evolution processes modeling multi-particle interferences, which directly determine scattering from disordered materials. I demonstrate the concept by examining network-based material classification, microstructure screening and preferential attachment in evolutions, which are applied to stealthy hyperuniformity. The results enable independent control of scattering from different length scales, revealing superdense material phases in short-range order. The proposed concept provides a bridge between wave physics and network science to resolve multiscale material complexities and open-system material design.

Protective consumption behavior under smog: using a data-driven dynamic Bayesian network.

In the midst of the deteriorating air pollution and collective stress, people pay close attention to risk mitigation measures such as keeping indoor and purchasing anti-smog products. Through impact evaluations, factors regarding health protective behavior can be identified. However, limited research is available regarding probabilistic interdependencies between the factors and protective behavior and largely relies on subjective diagnosis. These concerns have led us to adopt a data-driven static Bayesian network (BN) and Dynamic BN model to help explore multidimensional factors that may influence the public's health protective behavior of buying anti-smog air purifiers and examine the dependencies among network nodes. Using the city-level aggregate data from an online shopping platform, the results shed new light on relationships existing among 11 factors and protective behavior of buying air purifiers. Furthermore, taking into account the dynamic nature of protective behavior, we add time-related factors on the basis of static BN to construct the dynamic BN model. Results indicate that PM2.5 concentration and product price are the two leading factors affecting the consumption behavior for air purifiers. Additionally, media-related factors play an important role in the consumption behavior. This study contributes to the fields of impact evaluation of protective consumption behavior analysis and links environment risk with public consumption by identifying key factors.

Comparative Analysis of ANN-Based Mobility Prediction Performance in an Ad-Hoc Network

The facility of arbitrary node movement one side has advantages on application on the other side very difficult to manage the network because random node mobility directly effects on network connectivity and interrupt on the performance, obtained challenges like routing overhead, packet losses, increases energy consumption, wasted bandwidth for reconnection, decreases throughput etc. Thus an accurate mobility prediction of a node before leaving one position to another or subsequence position can be improve network performance which is effects by node mobility. Now day’s artificial neural networks (ANNs) is very common and trending for approximation and prediction application and also popular for node trajectory prediction. In this paper we explore the architectures of some static (like MLP and RBF) and dynamic (like FTDNN, DTDNN, NARX and LSTM) neural network and search best ANN model by obtaining optimal model parameters to predict node mobility and compared the performance using mobility model (Gauss Markov model) dataset as well as real-world dataset collected from Crawdad to highlight generalization capabilities. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and average coordinates distance error (DE) between observed and estimated positions are used to evaluate their performance. The empirical results show that LSTM is the best artificial neural network (ANN) model for mobility prediction in both model based and real-world dataset(testing sets).

Health behavior homophily can mitigate the spread of infectious diseases in small-world networks

Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.

Adaptive probabilistic load forecasting for individual buildings

Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters. It acts as a basis for arranging distributed energy resources, implementing demand response, etc. Compared to aggregated-level load, the electric load of an individual building is more stochastic and thus spawns many probabilistic forecasting methods. Many of them resort to artificial neural networks (ANN) to build forecasting models. However, a well-designed forecasting model for one building may not be suitable for others, and manually designing and tuning optimal forecasting models for various buildings are tedious and time-consuming. This paper proposes an adaptive probabilistic load forecasting model to automatically generate high-performance NN structures for different buildings and produce quantile forecasts for future loads. Specifically, we cascade the long short term memory (LSTM) layer with the adjusted Differential ArchiTecture Search (DARTS) cell and use the pinball loss function to guide the model during the improved model fitting process. A case study on an open dataset shows that our proposed model has superior performance and adaptivity over the state-of-the-art static neural network model. Besides, the improved fitting process of DARTS is proved to be more time-efficient than the original one.

Mathematical modeling of air distribution dynamics due to piston effect of trains in long railway tunnels

The stationary and nonstationary problems on air flow under piston effect of trains in long railway tunnels are solved using the methods of computational aerodynamics. The comparison of the results reveals erroneous calculation of air exchange in case of the stationary problem formulation. The validity of calculating tunnel ventilation by integration of the static air distribution network models and the quasi-dynamic models of piston effect is justified.

Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling

Improving the operation, management, and consequent performance of wastewater treatment plants (WWTPs) for conserving the water environment is crucial. Recent advancements in artificial intelligence (AI) modeling have shown the potential to solve the non-linear simulation of processes in WWTPs and facilitate real-time operational adjustments. In this study, a dynamic nonlinear autoregressive network with an exogenous input (NARX) model was established for predicting effluent quality. The performance was optimized with different time-delay parameters and training algorithms. Then, a PCA-NARX hybrid model was established for high performance and comparison with two static artificial neural network (ANN) models. The BR algorithm exhibited the highest performance among the four training algorithms for the NARX model. The dynamic PCA-NARX model was significantly superior to static models in modeling effluent quality. The PCA-NARX model predicted the effluent chemical oxygen demand (CODcr) and total nitrogen (TN) with high accuracy (RMSECOD = 2.9 mg/L, RMSETN = 0.8 mg/L). Therefore, we propose a stable and sensitive dynamic neural network model for predicting effluent quality and potential real-time adjustment of wastewater treatment operations.

On a heavy-tailed distribution and the stability of an equilibrium in a distributed delay symmetric network

We consider a static artificial neural network model endowed with multiple unbounded S-type distributed time delays. The delay kernels are described by the Pareto distribution, which is a heavy-tailed power-law probability distribution frequently employed in the characterisation of many observable phenomena. We give a characterisation of the effects of the shape and the scale of the Pareto delay distribution on the stability of an equilibrium of the network.

Dynamic hidden-variable network models.

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

Developing Multimodal Dynamic Functional Connectivity as a Neuroimaging Biomarker.

Background: In spite of increasing evidence highlighting the role of dynamic functional connectivity (FC) in characterizing mental disorders, there is a lack of (a) reliable statistical methods to compute dynamic connectivity and (b) rigorous dynamic FC-based approaches for predicting mental health outcomes in heterogeneous disorders such as post-traumatic stress disorder (PTSD). Methods: In one of the first such efforts, we develop a reliable and accurate approach for estimating dynamic FC guided by brain structural connectivity (SC) computed using diffusion tensor imaging data and investigate the potential of the proposed multimodal dynamic FC to predict continuous mental health outcomes. We develop concrete measures of temporal network variability that are predictive of PTSD resilience, and identify regions whose temporal connectivity fluctuations are significantly related to resilience. Results: Our results illustrate that the multimodal approach is more sensitive to connectivity change points, it can clearly detect localized brain regions with the dynamic network features such as small-worldedness, clustering coefficients, and efficiency associated with resilience, and that it has superior predictive performance compared with existing static and dynamic network models when modeling PTSD resilience. Discussion: While the majority of resting-state network modeling in psychiatry has focused on static FC, our novel multimodal dynamic network analyses that are sensitive to network fluctuations allowed us to provide a model of neural correlates of resilience with high accuracy compared with existing static connectivity approaches or those that do not use brain SC information, and provided us with an expanded understanding of the neurobiological causes for PTSD. Impact statement The methods developed in this article provide reliable and accurate dynamic functional connectivity (FC) approaches by fusing multimodal imaging data that are highly predictive of continuous clinical phenotypes in heterogeneous mental disorders. Currently, there is very little theoretical work to explain how network dynamics might contribute to individual differences in behavior or psychiatric symptoms. Our analysis conclusively discovers localized brain resting-state networks, regions, and connections where variations in dynamic FC (that is estimated after incorporating brain structural connectivity information) are associated with post-traumatic stress disorder resilience, which could potentially provide valuable tools for the development of neural circuit modeling in psychiatry in the future.