Network Topology Structure Research Articles

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

Comparisons between direct and embodied natural gas networks: Topology, dependency and vulnerability

Natural gas was a type of alternative energy resources in the short and medium term to maintain energy consumption worldwide. Combining multi-regional input-output analysis and complex network modelling, this study examined the spatial-temporal evolution and topological structures of multi-layer natural gas networks, analyzed the network characteristics of different countries/regions, and tested the network robustness and vulnerability between direct natural gas trade network and embodied natural gas transfer network. From the results, the direct natural gas trade network was relatively flatter, while the embodied natural gas transfer network was more concentrated in core countries/regions. Moreover, the countries with natural gas reserves and highly industrialized levels occupied high statuses in the direct natural gas trade network, whereas the highly industrialized countries occupied the core position in the embodied natural gas transfer network. Among the two disturbance scenarios, both direct and embodied natural gas networks were more vulnerable under deliberate assault than under random failure, however, the direct trade network is easier to collapse compared with the embodied transfer network under deliberate assault. By constructing multi-layer natural gas networks from both direct and embodied perspectives, this study attempts to provide practical implications and suggestions toward sustainable natural gas network management worldwide.

The power law statistics of the spiking timing in a neuronal network

Information encoding and decoding by neurons is a fundamental process in neuroscience. Herein, we present a statistical investigation of the first spike timing arising from the neuronal population in a small-world network with the Hodgkin–Huxley model after a neuron has received current stimulation, including a transient or a continuous stimulus. Regardless of how the interaction between neurons in the network was implemented, via electrical coupling or chemical synapses, we found the same power-law statistics for the first spike timing, independent of the topological structure of the neuronal network. We further suggest that such power-law statistics can be a generalized feature for the first spike timing in the small-world and scale-free neuronal networks. Our findings provide new insight into the coding mechanism for the first spike timing and improve the understanding of the power-law behavior in nature.

Recyclable, Self-Healing, and Highly Thermal Conductive Natural Rubber Nanocomposites Enabled by a Dynamic Covalent Network with Carboxylated Boron Nitride Nanosheets

It is of great significance for saving resources and protecting the environment to endow rubber with excellent recycling ability. Recyclable and self-healing ENR/BN-COOH/GO nanocomposites were prepared through dynamic covalent crosslinking between epoxidized natural rubber (ENR) and carboxylated boron nitride (BN-COOH), and graphene oxide (GO) was used as a reinforcing filler. The constructed dynamic β-hydroxyl ester bond between ENR and BN-COOH resulted in the remarkable enhancement of interfacial interaction and effective energy dissipation, which contributed to good mechanical properties of the nanocomposites, increasing by 116 and 79% in comparison with ENR and ENR/GO, respectively. An exchangeable β-hydroxyl ester bond could regulate a network topology structure via a transesterification reaction at the interface, endowing ENR with desirable recycling and self-healing ability. Moreover, the significant improvement of thermal conductivity for ENR/BN-COOH/GO nanocomposites was achieved, reaching 2.853 W/(m·K) at only 6 wt % BN-COOH content, indicating the formation of effective thermal conductive paths via the β-hydroxyl ester bond and hybrid thermal conductive network of BN-COOH/GO. Meanwhile, the thermal conductivity of recycled samples still maintained a high level due to the construction of the stable thermal conductive network. Such a facile strategy exhibits a promising perspective in preparing recyclable and highly thermal conductive rubber nanocomposites for applications in efficient thermal management fields.

A Goodwin Model Modification and Its Interactions in Complex Networks.

The global economy cannot be understood without the interaction of smaller-scale economies. We addressed this issue by considering a simplified economic model that still preserves the basic features, and analyzed the interaction of a set of such economies and the collective emerging dynamic. The topological structure of the economies' network appears to correlate with the collective properties observed. In particular, the strength of the coupling between the different networks as well as the specific connectivity of each node happen to play a crucial role in the determination of the final state.

Topology identification for stochastic multi-layer networks via graph-theoretic method

The topological structures of multi-layer networks have an important influence on their dynamical properties, but in most cases the topological structures of networks are unknown. Hence, this paper pays attention to investigating topology identification problems for multi-layer networks with stochastic perturbations. Both intra-layer coupling and inter-layer coupling are incorporated into the research model. Based on the graph-theoretic method and Lyapunov function, topology identification criteria for stochastic multi-layer networks are obtained by designing a suitable adaptive controller. Furthermore, to estimate the time of identification, the finite-time identification criteria are obtained by finite-time control technique. Finally, double-layer Watts–Strogatz small-world networks are presented for numerical simulations to illustrate the correctness of theoretical results.

Robustness of Consensus of Two-Layer Ring Networks

The topology structure of multi-layer networks is highly correlated with the robustness of consensus. This paper investigates the influence of different interlayer edge connection patterns on the consensus of the two-layer ring networks. Two types of two-layer ring network models are first considered: one is a kind of two-layer ring network with two linked edges between layers (Networks-a), and the other is a kind of two-layer ring network with three linked edges between layers (Networks-b). Using the Laplacian spectrum, the consensus of the network model is derived. The simulation experiments are used to demonstrate the influence of different interlayer edge connection patterns on the consensus of networks. To determine the best edge connection pattern for Networks-a and Networks-b, the number of nodes in a single-layer ring network is denoted by n. The best edge connection pattern for Networks-a is 1 & [(n+2)/2]. Furthermore, n is subdivided into 3k,3k+1,3k+2, and the best edge connection patterns of Networks-b are near 1 & k+1 & 2k+1.

Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)

Urban rail transportation is an important public transportation network for realizing the dual carbon strategy and sustainable transportation development. A top-stream station is an important node in this network. This paper focuses on 47 top-stream station sites during the peak hours of workdays from 2018 to 2020 in Beijing (26 inbound and 22 outbound, with Beijing South Station as a double-stream station) and collects and analyzes information such as station area spatial texture, spatial organization efficiency, land use function, land use mix, POI (Point of Interest) distribution and functional mix, construction intensity, and population heat distribution. Through an analysis of the network topological structure characteristics of each station area, comparison of spatial efficiency differences, analysis of land use function composition and mix characteristics, and distribution of spatial construction intensity, this paper discusses the relationship between the spatial structure, spatial function, spatial intensity characteristics and key indicators of the built environment of station areas from the perspective of urban design. The conclusion shows that there is a close relationship between the function, structure, and strength of the overall built environment of the station domain at the theoretical level. The regression test, to some extent, confirms the close relationship between key indicators and expands the indicator system for measuring the fit relationship. The comparison between general station sites and headstream station sites shows that the fit relationship of indicators for headstream station sites is not completely the same as that for general station sites, indicating that the influencing factors for headstream station sites are diverse. This reminds investment, design, construction, and management teams in practice that the measurement and planning of the built environment space structure of the station domain should be based on local conditions and be closely related to the topological structure of the station domain’s rail network and road network structure. At the same time, whether the stability of the indicator system and the specific R-squared value have differences in various cities requires further verification. This paper explores and tries to raise questions about the research methodology of the built environment space structure, the measurement of the station domain, and the concepts of station-city coordination and development.

Cascading failure invulnerability analysis of chemical material network considering failure propagation capability

AbstractThe material network in the chemical process has the characteristics of a complex network. Once the fault occurs, it will spread and lead to cascading failures, causing the chemical production process to not proceed normally. Previous studies of cascading failures have been limited by the size and characteristics of the network, which prevented the definition of the fault propagation capacity of the edge based on the actual situation. Studies on the effects of network topology and certain risk factors on the amount of fault propagation still need to be completed. Because of the above shortcomings, this study constructs a chemical material network cascading failure invulnerability analysis model that considers the failure propagation ability. Through the study of the chemical material network topology structure and the analysis of various flammable and explosive chemicals, the paper defines the fault propagation coefficient, which affects the load propagation quantity on the connecting edge. Then, based on the load‐capacity nonlinear cascading failure model, the chemical material network's invulnerability was analyzed by adjusting the model parameters. The effectiveness of the model is verified through case analysis. Compared to the existing model, the model in this paper effectively improves the invulnerability of the material network and can reduce the phenomenon of large‐scale failure of the material network due to the failure propagation capability is not considered.

Exponential stabilization of stochastic complex networks with Markovian switching topologies via intermittent discrete-time state observation control

This paper is concerned with the exponential stability of the stochastic complex networks with Markovian switching topologies. It is worth emphasizing that the topological structure of the stochastic complex networks is Markovian switching. Not all switching subnetworks must contain a spanning tree or be strongly connected. Moreover, a new type of aperiodically intermittent discrete-time state observation control is proposed, which is a significant extension of discrete-time state observation control and intermittent control. Based on the M-matrix, the Lyapunov method, and the graph theory, some sufficient conditions for exponential convergence. Moreover, we obtain that the average control rate in this paper is greater than the control rate proposed in the existing literature, which is less conservative. In addition, the theoretical results are used to discuss the exponential stability of stochastic coupled oscillators and a communication network model, respectively. Finally, two numerical examples are given to verify the effectiveness of the results.

An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection

Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA with more biological consistency. For this purpose, a new crossover operator is suggested where biological information in terms of both gene semantic similarity and protein functional similarity is fed into its design. To reflect the heuristic roles of both semantic and functional similarities, this paper introduces two gene ontology (GO) aware crossover operators. These are direct annotation-aware and inherited annotation-aware crossover operators. The first strategy is handled with the direct gene ontology annotation of the proteins, while the second strategy is handled with the directed acyclic graph (DAG) of each gene ontology term in the gene product. To conduct our experiments, the proposed EAs with GO-aware crossover operators are compared against the state-of-the-art heuristic, canonical EAs with the traditional crossover operator, and GO-based EAs. Simulation results are evaluated in terms of recall, precision, and F measure at both complex level and protein level. The results prove that the new EA design encourages a more reliable treatment of exploration and exploitation and, thus, improves the detection ability for more accurate protein complex structures.

Analysis of the consensus of double-layer chain networks

The multi-layer network topology structures directly affect the robustness of network consensus. The different positions of edges between layers will lead to a great difference in the consensus of double-layer chain networks. Finding the optimal positions of edges for consensus can help to design the network topology structures with optimal robustness. In this paper, we first derive the coherence of double-layer chain networks with one and two connected edges between layers by graph theory. Secondly, the optimal and worst connection edges positions of the two types of networks are simulated. When there is one edge between layers, the optimal edge connection position is found at 1/2 of each chain, and the worst edge connection position is found at the end node of the chain. When there are two edges between layers, the optimal edges connection positions are located at 1/5 and 4/5 of each chain respectively, and the worst edges connection positions are located at the end node of the chain and its neighbor node. Furthermore, we find that the optimal edge connection positions are closely related to the number of single-layer network nodes, and obtain their specific rules.

A complexity assessment framework with structure entropy for a cloud‐edge collaborative manufacturing system

AbstractThe Industrial Internet of Things (IIoT), along with 5G and beyond networks, is driving a new era of revolution in intelligent manufacturing. However, the integration of more heterogeneous entities and intricate communication protocols complicates the enhanced manufacturing system, posing challenges for quantitatively assessing its complexity. To tackle this issue, a complexity assessment framework for the IIoT‐enabled collaborative manufacturing system is proposed by combining the complex network and information entropy theory. Firstly, industrial entities in the physical space are mapped into a two‐tier complex network taking into account the weights of various access communications. Secondly, an importance‐aware structure entropy is introduced to capture the complexity of industrial networks from the communication perspective in the system. The experiments conducted on various network topological structures validate the proposed method and provide guidance for system design.

Identifying covariate-related subnetworks for whole-brain connectome analysis.

Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive noise and almost infinite possibilities of edges combining into subnetworks. To address these challenges, we propose a new statistical approach to handle multivariate edge variables as outcomes and output covariate-related subnetworks. We first study the graph properties of covariate-related subnetworks from a graph and combinatorics perspective and accordingly bridge the inference for individual connectome edges and covariate-related subnetworks. Next, we develop efficient algorithms to exact covariate-related subnetworks from the whole-brain connectome data with an $\ell_0$ norm penalty. We validate the proposed methods based on an extensive simulation study, and we benchmark our performance against existing methods. Using our proposed method, we analyze two separate resting-state functional magnetic resonance imaging data sets for schizophrenia research and obtain highly replicable disease-related subnetworks.

Robustness analysis of highway network based on complex network

Taking Chongqing, Hunan, Shandong, Shaanxi and Sichuan as examples, this paper conducts a comparative study on the robustness of their highway networks, which is helpful for the subsequent construction of China's highway networks. The topology structure of highway networks is studied by complex network theory. The degree distribution, average degree, average clustering coefficient, average path length, network diameter and other parameters of the network were calculated, and the robustness of the highway network in five provinces and cities was compared from four aspects: connectivity, network efficiency, turn rate and robustness r. The results show that Chongqing and Shaanxi have a good performance of highway network robustness, Shandong and Sichuan have a more balanced performance, and Hunan has a weak performance. Enhance network robustness by placing route planning directions in place with fewer route options to provide drivers with more route options.

Finite-time topology identification of stochastic delayed coupled systems on multi-weighted networks based on graph-theoretic method

This paper investigates topology identification problem for stochastic delayed coupled systems on multi-weighted networks. To shorten control time, finite-time control technique is adopted in this article. Based on the graph theory, Lyapunov method and stochastic analysis, finite-time topology identification criteria are derived. Furthermore, to identify partial topological structures of large-scale networks over finite-time domain, pinning control is also considered in this paper. Therein, the unknown topological structures and partial topological structures are identified successfully under the corresponding controllers. Finally, the effectiveness of analytical results is given by two representative numerical examples.

ENERGY SAVING THROUGH IMPROVED TOPOLOGY OF THE CITY WATER NETWORK

This article compares the operation of the existing water supply system in the city Kremenchuk, Poltava region, namely the giving-distributive complex (GDC), which includes the main water network and the pumping station of the 2-nd lift, with the GDC, in which the topological structure has been improved by introducing a new section of the network. During the operation of the GDC, the tasks of repairing or replacing pipes of individual sections the water network constantly arise. At the same time, attention should also be paid to the topological structure of the water network during its reconstruction. The need to improve the topological structure is clearly seen after performing hydraulic calculations and building piezometric maps. For comparison, piezometric maps need to build before and after the reconstruction of the water network. At the nodal points of the network, a geodetic mark, free pressure and piezometric mark are shown. Considering that water moves from a larger piezometric mark to a smaller one, the directions of water movement and water network nodes with minimal free pressures are determined. At the same time, flaws in the topological structure are found on the piezomap, which should be eliminated in order to increase its dependebility. It is known that dependebility includes 3 components: reliability, maintainability and durability. Durability is ensured by the choice of pipe material during design. If we take into account simultaneously reliability and maintainability, then a complex indicator should be used, namely, the availability factor Ka. It estimates the probability that at any given time the section of water network is in working condition. According to the topological structure, the water network of the city should be looped and have more connections between nodes. But in practice, this principle can not always be maintained, given the planning structure of the streets the city. Graphical representation of hydraulic calculations using piezomaps allows you to identify overloaded sections of the water network and increase its dependebility. When improving the topological structure, it is necessary to use the prof. Novokhatniy principle, namely, new sections must be introduced so that they unite the existing nodes of the existing water network. Hydraulic calculations have shown that by improving the topological structure of the water network the city Kremenchuk, the annual energy saving is about 452 thousand kWh, which corresponds to a saving of about UAH 2.7 million per year (at the cost of electricity 6 UAH per 1 kWh). Keywords: water supply system, giving-distributive complex, water network, topology, energy saving.

Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting

Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail to simultaneously capture the short-term and long-term temporal relations carried by the traffic flow data, and also suffer the over-smoothing problem. To overcome the problems, we propose a hierarchical traffic flow forecasting network by merging newly designed the long-term temporal Transformer network (LTT) and the spatio-temporal graph convolutional networks (STGC). Specifically, LTT aims to learn the long-term temporal relations among the traffic flow data, while the STGC module aims to capture the short-term temporal relations and spatial relations among the traffic flow data, respectively, via cascading between the one-dimensional convolution and the graph convolution. In addition, an attention fusion mechanism is proposed to combine the long-term with the short-term temporal relations as the input of the graph convolution layer in STGC, in order to mitigate the over-smoothing problem of GCN. Experimental results on three public traffic flow datasets prove the effectiveness and robustness of the proposed method.

A MINLP model for the layout design of subsea oil gathering - transportation system in deep water oil field considering avoidance of subsea obstacles and pipe intersections

This paper focuses on the layout design of subsea oil gathering-transportation system in deep water oil field. In such a system, subsea manifolds are applied to gather and transport the produced fluids from subsea wells to floating processing terminals. A mixed integer nonlinear programming (MINLP) model is proposed, constrained by a series of operation and production requirements, aiming to minimize the total layout cost. Through the model, the pipe line network topology structure which reflects the allocations among the subsea wells, manifolds and processing terminals, the routes of pipes, as well as the size of the facilities could all be figured out. Two key contributions are made through this work. First, avoiding pipe intersections and subsea obstacles are integrated simultaneously, making the proposed model closer to practical situations. Second, a decomposition strategy based on Delaunay triangulation and gradient descent is constructed, achieving high quality initial solution and stable iteration process. The results of case studies indicate the validity, feasibility and stability of the proposed model and the solution method. Besides, the effect of manifold numbers on the layout optimization result is analyzed through the model, indicating its flexibility in the layout design analysis.

Structural alterations of the salience network in patients with insular glioma.

The structural alteration that occurs within the salience network (SN) in patients with insular glioma is unclear. Therefore, we aimed to investigate the changes in the topological network and brain structure alterations within the SN in patients with insular glioma. We enrolled 46 patients with left insular glioma, 39 patients with right insular glioma, and 21 demographically matched healthy controls (HCs). We compared the topological network, gray matter (GM) volume, and fractional anisotropy (FA) between HCs and patients after controlling for the effects of age and gender. Patients with insular glioma showed topological network decline mainly in the insula, basal ganglia region, and anterior cingulate cortex (ACC). Compared with HCs, patients primarily showed GM volume increased in the ACC, inferior temporal gyrus (ITG), superior temporal gyrus (STG), temporal pole: middle temporal gyrus (TPOmid), insula, middle temporal gyrus (MTG), middle frontal gyrus, and superior occipital gyrus (SOG), but decreased in TPOmid, ITG, temporal pole: superior temporal gyrus, and SOG. FA declined mainly in the STG, MTG, ACC, superior frontal gyrus, and SOG, and also showed an increased cluster in SOG. FA represents the integrity of the white matter. In patients with insular glioma, decreased FA may lead to the destruction of the topological network within the SN, which in turn may lead to the decrease of network efficiency and brain function, and the increase of GM volume may compensate for these changes. Overall, this pattern of structural changes provides new insight into the compensation model of insular glioma.