Complex Network Topology Research Articles

Effective elastic properties of loosely connected fibrous bio-inspired materials

Understanding the mechanics of networks has become critical with the rapid development of advanced technologies that produce metamaterials with unique structures and properties. Architectured materials with random fibrous network microstructure may mimic the extracellular matrix (ECM), the low-connected microenvironment of biological cells. Elastic properties of ECM and artificial scaffolds influence the cells' interaction and state. However, experimental determination of these properties requires significant resources, whereas complex network topology complicates their prediction from theoretical analysis.In this work, we propose a way to determine the effective elastic properties of the fibrous hyperelastic material with a low degree of connectivity. A two-dimensional representative volume element (RVE) with a loosed fiber connectivity (C = 3.5) was quasi-statically subjected to uniaxial and equibiaxial tensions. The resulting material properties of fiber RVE were used as a reference for the continuum model simulations. Thus, the effective moduli of elasticity in two orthogonal directions for discrete fibrous and continuum models were evaluated and compared. Final results show that the model exhibits intense deformation and anisotropic properties appear starting from small deformations. Despite the highly non-linear behavior of the system, the Ogden model accurately described the system's behavior at the macro level.

Alternating chimera states in complex networks with modular structures.

Chimera, the coexistence state of synchronization and non-synchronization, widely exists in complex networks. It has a great potentially explanatory power for the unihemispheric sleep of birds and some mammals, in which the synchronizations of the hemispheres of the cerebral cortex are evolving alternately. In this study, a coupled nonlinear oscillator system with a topology of the modular complex network was constructed to simulate the left and right hemispheres of the brain. The results showed that a stable chimera, an alternating chimera, and a breathing chimera were produced when the coupling strength and connection probability of the left and right hemispheres were changed. Further, we studied the effect of noise on rich synchronous patterns and found that the alternating chimera was robust to Gaussian white noise when the strength was not very large. Finally, our study was extended to a complex network with three sub-networks, and an alternating chimera could exist in two or three sub-networks. Our research provides a deeper insight into the mechanism of brain function like unihemispheric sleep.

A Quantitative Risk Assessment Model for Distribution Cyber-Physical System Under Cyberattack

An accurate and comprehensive risk assessment in a distribution cyber-physical system (DCPS) is essential to ensure its smooth operation, effective control, and exposure of hidden dangers and security implications. Unfortunately, prior risk assessment methods are mostly limited in two aspects. First, the current studies do not respect the complex network topology and intertwined devicewise dependencies, thereby failing to delineate and model the risk propagation mechanism in DCPS accurately. Second, the prior work tends to focus on gauging the risks underlying physical systems independently, overlooking the quantification of risk impact on physical systems incurred by the cyberattack. To fill the gap, in this article, we propose a unified risk assessment model that gauges the comprehensive security implication of DCPS in a Bayesian regime, in which the three key components, namely, the prior probability, posterior probability, and minimum load-loss ratio, are calculated via the cumulative densities, epidemic model, and optimal load shedding, respectively. We benchmark our proposed model on a public testbed, IEEE 39-bus system, with extensive experiments. The results substantiate the viability and effectiveness of our model and suggest that the vulnerability of DCPS is positively correlated with the three components in our Bayesian modeling. We hope this finding can shed some light on future research by providing a plausible apparatus for measuring the security implications of physical systems in DCPS under cyberattacks.

Financial ripple effect in complex adaptive supply networks: an agent-based model

Tightening lending standards are motivating companies to adopt supply chain financing, with invoice backed lending to remedy financial stress. These financial objects depend on company-to-company relationships. The accumulation of these dyadic relationships creates complex supply network topologies. Companies within these networks are selfish and have varying degrees of bargaining power. To remain operational, they maximise their liquidity by negotiating longer repayment terms and cheaper financing, thus distributing risk onto weaker companies and propagating financial stress. To study this phenomenon, we created an agent-based supply network simulation model capturing these behaviours. We investigate structural conditions that make supply networks vulnerable to financial stress propagation and the resultant financial ripple effects using survivability analysis. We found firms with higher bargaining power are disproportionately more exposed to network risk. In diamond-shaped networks, firms occupying lower tiers are critical in financial stress propagation, becoming deep-tier nexus suppliers. Our results are relevant to industries with heterogeneous network composition. Practitioners must mitigate the effects of vulnerable network structures with careful supply chain financing design.

Neural Network Optimization Based on Complex Network Theory: A Survey

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.

Terminal Node of Active Distribution Network Correlation Compactness Model and Application Based on Complex Network Topology Graph

Multiple nodes (such as distributed generation (DG), electric vehicles (EV), energy storage (ES), flexible loads (FL), etc.) are connected to the active distribution network (ADN), which changes its original operational mode. According to the bidirectional current and low-voltage transmission mode, this study proposed a multi voltage and multi electricity flat loop network, AC/DC (Alternating Current/ Direct Current) hybrid network, unified interface and flexible self-organizing network based on Complex network theory. First, the ADN complex network topology of various nodes is established based on the actual grid connected terminal nodes and power flow sensitivity algorithm. Second, using the TOPSIS model, the influence factor matrix of weighted directed network is established. The matrix can be used to guide the formulation of the distribution network operation mode, and the robustness and reliability of this paper are verified by using the standard multi voltage level main distribution hybrid model provided by the Panda Power website as the verification method. Finally, using the influence maximization calculation model of the New Creedy algorithm, the node correlation matrix is expanded to form a super family region set of active distribution network. The results show that the seven nodes in this paper have high correlation, while the other nodes have low correlation. In addition, the change of reactive power has little impact on other nodes, for a node with a change rate of 0, it is obviously not in the same power supply family as node 1, and theoretically it may not have a topological relationship, be a power generation node, or be completely independent. Analyzing the relationship between nodes has a guiding significance for power supply recovery and interaction in distribution network reconfiguration.

Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons

BackgroundBuilding biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging.ResultsIn this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling.ConclusionWe have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.

Calculating voltage feasibility boundaries for power system security assessment

Modern power systems face a grand challenge in grid management due to increased electricity demand, imminent disturbances, and uncertainties associated with renewable generation, which can undermine grid security. The security assessment is directly connected to the robustness of the operating condition and is evaluated by analyzing proximity to the Power Flow (PF) solution space’s boundary. Calculating the location of such a boundary is a computationally challenging task, linked to the PF equations’ non-linear nature, presence of technical constraints, and complex network topology. This paper introduces a general framework to characterize points on the PF solution space boundary in terms of auxiliary variables subject to algebraic constraints. Then we develop an adaptive continuation algorithm to trace 1-dimensional sections of boundary curves which exhibits robust performance and computational tractability. Implementation of the algorithm is described in detail, and its performance is validated on different test networks.

OnSRAM: Efficient Inter-Node On-Chip Scratchpad Management in Deep Learning Accelerators

Hardware acceleration of Artificial Intelligence (AI) workloads has gained widespread popularity with its potential to deliver unprecedented performance and efficiency. An important challenge remains in how AI accelerators are programmed to sustain high utilization without impacting end-user productivity. Prior software optimizations start with an input graph and focus on node-level optimizations, viz. dataflows and hierarchical tiling, and graph-level optimizations such as operation fusion. However, little effort has been devoted to inter-node on-chip scratchpad memory (SPM) management in Deep Learning (DL) accelerators, whose significance is bolstered by the recent trends in complex network topologies and the emergence of eager execution in DL frameworks. We characterize and show that there exists up to a 5.2× performance gap in DL inference to be bridged using SPM management and propose OnSRAM, a novel SPM management framework integrated with the compiler runtime of a DL accelerator. We develop two variants, viz. OnSRAM-Static, which works on static graphs to identify data structures that can be lucratively held on-chip based on their size, liveness and significance, and OnSRAM-Eager, which targets an eager execution model (no graph) and uses a history-based speculative scheme to hold/discard data structures. We integrate OnSRAM with TensorFlow and analyze it on multiple accelerator configurations. Across a suite of 12 images, objects, and language networks, on a 3 TFLOP system with a 2 MB SPM and 32 GBps external memory bandwidth, OnSRAM-Static and OnSRAM-Eager achieve 1.02–4.8× and 1.02–3.1× reduction in inference latency (batch size of 1), over a baseline with no SPM management. In terms of energy savings, we observe average reductions of 1.51× (up to 4.1×) and 1.23× (up to 2.9×) for the static and eager execution scenarios, respectively.

M-PolKA: Multipath Polynomial Key-Based Source Routing for Reliable Communications

Innovative traffic engineering functions and services require disrupting routing and forwarding mechanisms to be performed with low overhead over complex network topologies. Source routing (SR) is a prominent alternative to table-based routing for providing the needed expressiveness and agility by reducing the number of network states. This work proposes the M-PolKA, a topology-agnostic multipath source routing scheme and orchestration architecture for reliable communications, which explores special properties from the Residue Number System (RNS) polynomial arithmetic. A P4-based proof-of-concept is experimentally demonstrated using emulated and hardware prototypes. Also, use cases for revealing M-PolKA’s functionalities are tested in different scenarios in order to address problems, such as communication reliability improvement, agile path migration and fast failure reaction. Finally, low overhead for extra functionalities is observed when RNS-based SR is compared to traditional routing approaches.

Topology Prediction and Structural Controllability Analysis of Complex Networks Without Connection Information

In this article, we consider complex networks without connection information. The absence of global structure induces a great obstacle in the structural controllability analysis of these networks. Thus, a topology predicting method based on the connection probability matrix is proposed to provide a global structure for structural controllability analysis in this article. Furthermore, the modified principles of predicting global network topologies are established to acquire a more accurate global connection relationship. Eventually, the drive node set of these networks is determined by predicting global topologies. The accuracy of the proposed topology predicting method is verified by numerical simulations in the context of artificial networks and real networks. The results reveal that the global topology and structural controllability of complex networks with large scale and high edge density could be accurately predicted by utilizing the proposed method.

Implementation of Quantum Key Distribution network simulation in Quantum Channel

The aim is to analyze the efficiency of a communication method, Quantum key distribution. This newly popularized method of communication uses the principles of quantum mechanics. The simulation environment allows researchers to create complex network topologies and a high degree of control and repeatable experiments, allowing them to conduct studies and verify their outcomes. It would be costly to implement QKD in reality, which would need optical and Internet connections between network nodes and the verification of a certain network method or protocol. Thus, the performance analysis here is done in open-source software called NS-3 or Network Simulator- 3, which has an in-built module called QKD netsim for creating the quantum channel.

Evolution of the Complex Network Topology of Producer Services and the Dynamic Upgrading of Manufacturing Value Chain in Guangdong-Hong Kong-Macao Greater Bay Area

In the context of close integration of economic globalization and of local production networks, the network topology of producer service formed by the linkage between producer service and manufacturing industry has attracted widespread concern. Based on the research perspective of complex network theory, this paper theoretically analyzes the impact mechanism of how the evolution of network topology of producer service promotes the upgrading of manufacturing’s value chain. In addition, taking the Guangdong-Hong Kong-Macao Greater Bay Area as research object, this paper further analyzes the evolution characteristics of network topology, such as the breadth, intensity and centrality of network connection of producer service, so as to identify the interactive efficiency between producer services and the upgrading of manufacturing value chain. Finally, this paper proposes the path of coordinated development of network topology of producer services and manufacturing value chain.

Identifying partial topology of complex networks with stochastic perturbations and time delay

In various practical scenarios, the topology of a complex network is generally unknown or unavailable. Based on the adaptive feedback control strategy and the pining mechanism, some controllers and observers are designed in this manuscript to identify the partial topology of the noise-contaminated networks with coupling delay. The approach can be extended to infer the whole topology of the networks. For large-scale complex networks, our approach greatly reduces the calculation compared with the previous methods. Numerical simulations are employed to illustrate the effectiveness of the proposed theoretical results.

A relaxed support vector data description algorithm based fault detection in distribution systems

The power detection of the distribution network is essential for reliable and secure distribution. In this paper, a flexible dual-threshold SVDD fault warning algorithm with fault samples is proposed to deal with problems concerning complex network topology, accessible data, and missing fault data in the power grid. For the problems of complicated network topology and a wide variety of signal types, we propose to combine wavelet packet energy features with Spearman to extract electric signal features, and finally achieve accurate feature extraction of multiple signal types. In the case of the problems of untimely judgement and low accuracy of the original SVDD, a relaxed SVDD fault warning algorithm with fault samples is correspondingly proposed. We turn the original SVDD boundary into a double-layer boundary, and divides the hypersphere space into three regions to increase the sensitivity to the fault samples and lessen the risk of missed detection. Besides, an adaptive update strategy is developed, which reduces the computational effort of the model and is proven more applicable to the distribution. Finally, the method is applied to numerical examples and fault detection experiments, and the experimental results in turn verified its effectiveness and superiority.

A Network Attack Risk Control Framework for Large-Scale Network Topology Driven by Node Importance Assessment

In large-scale network scenarios, network security data are characterized by complex association and redundancy, forming network security big data, which makes network security attack and defense more complicated. In this paper, the authors propose a framework for network attack risk control in large-scale network topology, called NARC. Using NARC, a user can determine the influence level of different nodes on the diffusion of attack risk in complex network topology, thus giving optimal risk control decisions. Specifically, this paper designs a topology-oriented node importance assessment model, combined with node vulnerability correlation analysis, to construct a diffusion network of attack risks for identifying potential attack paths. Furthermore, the optimal risk control node selection method based on game theory is proposed to obtain the optimal set of defense nodes. The experimental results demonstrate the feasibility of the proposed NARC, which helps to ease the risk of network attacks

Self-organization of in vitro neuronal assemblies drives to complex network topology

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

Integrating multi-omics data to identify dysregulated modules in endometrial cancer.

Cancer is generally caused by genetic mutations, and differentially expressed genes are closely associated with genetic mutations. Therefore, mutated genes and differentially expressed genes can be used to study the dysregulated modules in cancer. However, it has become a big challenge in cancer research how to accurately and effectively detect dysregulated modules that promote cancer in massive data. In this study, we propose a network-based method for identifying dysregulated modules (Netkmeans). Firstly, the study constructs an undirected-weighted gene network based on the characteristics of high mutual exclusivity, high coverage and complex network topology among genes widely existed in the genome. Secondly, the study constructs a comprehensive evaluation function to select the number of clusters scientifically and effectively. Finally, the K-means clustering method is applied to detect the dysregulated modules. Compared with the results detected by IBA and CCEN methods, the results of Netkmeans proposed in this study have higher statistical significance and biological relevance. Besides, compared with the dysregulated modules detected by MCODE, CFinder and ClusterONE, the results of Netkmeans have higher accuracy, precision and F-measure. The experimental results show that the multiple dysregulated modules detected by Netkmeans are essential in the generation, development and progression of cancer, and thus they play a vital role in the precise diagnosis, treatment and development of new medications for cancer patients.

A Multi-Agent-Based Simulation Model for the Spreading of Diseases Through Social Interactions During Pandemics

Epidemiological models have a vital and consolidated role in aiding decision-making during crises such as the Coronavirus Disease 2019 (COVID-19) pandemic. However, the influence of social interactions in the spreading of communicable diseases is left aside from the main models in the literature. The main contribution of this work is the introduction of a probabilistic simulation model based on a multi-agent approach that is capable of predicting the spreading of diseases. Our proposal has a simple model for the main source of infections in pandemics of respiratory viruses: social interactions. This simplicity is key for incorporating complex networks topology into the model, which is a more accurate representation for real-world interactions. This flexibility in network structure allows the evaluation of specific phenomena, such as the presence of super-spreaders. We provide the modeling for the dynamical network topology in two different simulation scenarios. Another contribution is the generic microscopic model for infection evolution that enables the evaluation of impact from more specific behaviors and interventions on the overall spreading of the disease. It also enables a more intuitive process for going from data to model parameters. This ease of changing the infection evolution model is key for performing more complete analyses than would be possible in other models from the literature. Further, we give specific parameters for a controlled scenario with quick testing and tracing. We present computational results that illustrate the model utilization for predicting the spreading of COVID-19 in a city. Also, we show the results of applying the model for assessing the risk of resuming on-site activities at a collective use facility.

Regional demarcation of synergistic control for PM2.5 and ozone pollution in China based on long-term and massive data mining

Implementing an inter-regional synergistic control policy for fine particulate matter (PM2.5) and ground-level ozone (O3) could improve regional air quality. However, little is known about the effectiveness and accuracy of synergistic control region delineation. This study aimed to construct a network model and apply it to a case study of regional delineation in China at different scales to quantify the interactions between regions. Firstly, the Cumulative Risk Index (CRI) was proposed and quantified from a health risk perspective based on the daily mean PM2.5 and daily maximum 8-h average O3 concentrations from 2015 to 2020 in China. Then, the complex network topology parameters were introduced to determine the optimal threshold for different network constructions, and the Girvan–Newman (GN) algorithm was used to divide the network into independent regions. Results showed that the correlation between cities is more robust than that between provinces. There are four-seven major provincial-scale regions with strong synchronicity in CRI, suggesting that PM2.5 and O3 synergistic control policies shall be implemented jointly within these demarcated regions. Moreover, urban-scale CRI network analysis indicated that the existing key control areas (2 + 26 cities) need to be expanded to 40–50 cities and refined into seven independent urban regions. Meanwhile, the Fen–Wei Plain can be focused on six cities: Xi'an, Baoji, Xianyang, Weinan, Yuncheng, and Tongchuan. This study could improve our understanding of the synergistic control regions for PM2.5 and O3 pollution, and the results could be used to develop joint control policies for both pollutants.