Dynamic Node Research Articles

Exploring synchronizability of complex dynamical networks from edges perspective

With the rapid development of network information technology, synchronization problem of complex dynamical networks has garnered extensive attention. Current research predominantly concentrates on analyzing synchronizability and synchronization control in the complex dynamical networks with static edge weights or single weight attribute connections. However, there is limited research on networks characterized by variable weights and multiple weighted connections. This study addresses this gap by analyzing the synchronizability of such complex networks. In this paper, we investigate the synchronizability of two types of networks: weight-variable complex dynamical networks and multi-weighted complex dynamical networks. Considering the evolution of network weights, the synchronizability of complex dynamical network with weight-variable is investigated, the master stability equation, synchronizability criteria of the network are derived, and experiments are used to substantiate the validity of our findings. Our study then shifts to multi-weighted complex networks. Take the Rössler oscillators as node dynamics, we explore the impact of different inner coupling matrices on synchronization types under both unknown and known topologies. Moreover, we extend our study to scenarios with different coupling strengths. The analysis reveals that the inner coupling matrix influences the network’s synchronization region type. Interestingly, the clarity or ambiguity of network topologies does not markedly affect the synchronization region type. Furthermore, our analysis indicates a correlation between the synchronization region boundaries of multi-weighted complex networks and those of their single-weighted counterparts capable of synchronization.

Complex networks after centrality-based attacks and defense

Exploration in complex networks has surged. Centrality measures play a pivotal role in pinpointing essential components within these networks. Previous work focus on nodes with the highest Betweenness centrality through extensive simulations. This paper analyzes the attack and/or defense strategy using one more centrality metric, bridging centrality and Bridging-Betweenness Fusion Attack (combination of both betweenness and bridging centrality). Our two-fold contribution is (1) Using high centrality removal as an attacking strategy and inspired by the dynamic node removal process, recalculated node method after each node removal is proposed. (2) In our defense techniques, new nodes are added to existing lower centrality nodes. They are added after attacks to restore the graph’s connectivity according to proposed defense strategies. Note that some attacks and defense techniques were already introduced while others are presented first time, e.g., the combination of two centrality measures for attack and a bridging-based defense strategy. This innovative approach presents a promising advancement in enhancing the resilience and fortification of complex networks against potential attacks, signifying a notable advantage of this work.

Network Evolution Model with Preferential Attachment at Triadic Formation Step

It is recognized that most real systems and networks exhibit a much higher clustering with comparison to a random null model, which can be explained by a higher probability of the triad formation—a pair of nodes with a mutual neighbor have a greater possibility of having a link between them. To catch the more substantial clustering of real-world networks, the model based on the triadic closure mechanism was introduced by P. Holme and B. J. Kim in 2002. It includes a “triad formation step” in which a newly added node links both to a preferentially chosen node and to its randomly chosen neighbor, therefore forming a triad. In this study, we propose a new model of network evolution in which the triad formation mechanism is essentially changed in comparison to the model of P. Holme and B. J. Kim. In our proposed model, the second node is also chosen preferentially, i.e., the probability of its selection is proportional to its degree with respect to the sum of the degrees of the neighbors of the first selected node. The main goal of this paper is to study the properties of networks generated by this model. Using both analytical and empirical methods, we show that the networks are scale-free with power-law degree distributions, but their exponent γ is tunable which is distinguishable from the networks generated by the model of P. Holme and B. J. Kim. Moreover, we show that the degree dynamics of individual nodes are described by a power law.

Phase chimera states on nonlocal hyperrings.

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to studying chimera states in systems of identical oscillators, nonlocally coupled through pairwise interactions. Nevertheless, there is increasing evidence, also supported by available data, that complex systems are composed of multiple units experiencing many-body interactions that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a topology solely involving many-body, nonlocal, and nonregular interactions, hereby named nonlocal d-hyperring, (d+1) being the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and we show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are "flattened" to pairwise ones, the chimera behavior is weaker and more elusive.

Complex Couplings - A Universal, Adaptive, and Bilinear Formulation of Power Grid Dynamics

The energy transition introduces new classes of dynamical actors into the power grid. There is, in particular, a growing need for so-called grid-forming inverters (GFIs) that can contribute to dynamic grid stability as the share of synchronous generators decreases. Understanding the collective behavior and stability of future grids, featuring a heterogeneous mix of dynamics, remains an urgent and challenging task. Two recent advances in describing such modern power grid dynamics have made this problem more tractable. First, the normal form for grid-forming actors provides a uniform technology-neutral description of plausible grid dynamics, including grid-forming inverters and synchronous machines. Second, the notion of the complex frequency has been introduced to effortlessly describe how the nodal dynamics influence the power flows in the grid. The major contribution of this paper is to show how the normal-form approach and the complex-frequency dynamics of power grids combine and how they relate naturally to adaptive dynamical networks and control-affine systems. Using the normal form and the complex frequency, we derive a remarkably elementary and universal equation for the collective grid dynamics. Notably, we obtain an elegant equation entirely in terms of a matrix of complex couplings, in which the network topology does not appear explicitly. These complex couplings give rise to new adaptive network formulations of future power grid dynamics. We give a new formulation of the Kuramoto model, with inertia as a special case. Starting from this formulation of the grid dynamics, the question of the optimal design of future grid-forming actors becomes treatable by methods from affine and bilinear control theory. We demonstrate the power of this perspective by deriving a quasilocal control dynamics that can stabilize arbitrary power flows, even if the effective network Laplacian is not positive definite. Published by the American Physical Society 2024

The Hopf whole-brain model and its linear approximation

Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation—that holds in the case of heterogeneous parameters and time-delays—allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.

Dynamic relay node selection and routing for cloud-native Software Defined WANs

Cloud Native Software Defined Wide Area Networks (CN-WANs) are proposed to optimize the network performance based on the application requirement. In this paper, we propose a Relay Node Selection and Routing (RNSR) approach to minimize the number of relay nodes in CN-WANs under the end-to-end (E2E) transmission latency constraint. By doing so, we can not only ensure the Service Level Agreement (SLA) to applications, but also reduce the network operation expenses. To improve the system scalability, several algorithms are proposed for networks with different sizes. According to experiments on a prototype built on EC2 and extensive simulations in a custom in-house simulator, we demonstrate that RNSR has a superior performance on reducing the number of required relay nodes, outperforming the state-of-the-art benchmarks by up to 92.48% specifically. In addition, the algorithms designed for large scale networks will not suffer too much performance degradation but significantly improve the system scalability.

ConTIG: Continuous representation learning on temporal interaction graphs

Representation learning on temporal interaction graphs (TIG) aims to model complex networks with the dynamic evolution of interactions on a wide range of web and social graph applications. However, most existing works on TIG either (a) rely on discretely updated node embeddings merely when an interaction occurs that fail to capture the continuous evolution of embedding trajectories of nodes, or (b) overlook the rich temporal patterns hidden in the ever-changing graph data that presumably lead to sub-optimal models. In this paper, we propose a two-module framework named ConTIG, a novel representation learning method on TIG that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploits three-fold factors in dynamic networks including latest interaction, neighbor features, and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes’ state trajectories from time-adjacent interaction patterns using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiment results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation, and dynamic node classification tasks of four datasets compared with a range of state-of-the-art baselines, especially for long-interval interaction prediction.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated. On the other hand, multi-layer complex network exists widely in practical situations. In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied. A suitable functional state observer is constructed with the limited measurement. The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem. Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained. Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states. Thus, it can greatly reduce the placement of observers and computational cost. Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Surveillance or Dynamic Sentinel Lymph-Node Biopsy in Low-Risk Clinically N0 Penile Squamous Cell Carcinoma: Single-Institution Real World Data

IntroductionSurveillance is the standard management in low-risk cN0 penile squamous cell carcinoma (peSCC) patients. However, no previous analysis focused on early and long-term outcomes of these patients. We report on main oncological outcomes of a large series of low-risk cN0 peSCC patients. Patients and MethodsBetween 1980 and 2017 included, 93 evaluable consecutive low-risk (ie, pT1a G1 cN0M0) peSCC patients underwent primary tumor surgery and either observation (74) or dynamic sentinel node biopsy (DSNB) (19) following a clinical diagnosis of T1 in 66 (71%), T2 in 15 (16.1%) and Tx in 12 (12.9%) patients, respectively. The statistical significance of differences in medians and proportions was tested with the Kruskal-Wallis and chi-square tests. Kaplan-Meier plots illustrated 5-year inguinal relapse (IR)-free survival rates. ResultsMedian age was 60 years (IQR: 50-69 years). Median follow-up was 92 months (IQR 54-133 months). Surveillance was more frequently adopted in clinical (c)T1 than in cT2 tumors (79.7% vs. 36.8%). None of 19 patients who had DSNB had nodal metastasis. Overall, 7 (7.5%) out of 93 pT1aG1cN0 peSCC patients had IR after a median interval of 9 months. Of note, 1 patient only relapsed after 12 months of surveillance. After stratification according to IR, relapses occurred more frequently in younger patients (59 vs. 64 years, P < .001). The 5-year IR-free survival rates for the entire cohort was 92% (95% Confidence interval [CI] 87-98%). ConclusionsObservation is a safe and effective management for low-risk peSCC patients. Younger patients may be offered a mini-invasive staging as an alternative.

Study on Inverse Analysis Technique Considering Influence of Construction Procedure on Performance of Large-Span Steel Latticed Arch

In this paper, the influence of the construction process of large-span steel latticed arch on the geometric configuration and bearing capacity performance of the large-span steel latticed arch is calculated and analyzed by using the inverse analysis technique. By comparing the calculation results of single pre-deformation adjustment of the node co-ordinates in zero state of the whole structure and the dynamic adjustment of the node co-ordinates of the unconstructed part of the structure, it is found that the inverse analysis method of dynamic node co-ordinate adjustment is used to achieve the unification of the geometric configuration of the final structure and the bearing capacity performance of the structure. The results show that the inverse analysis method of dynamic node co-ordinate adjustment proposed in this paper can simulate and track the dynamic changes of structural geometry and stable bearing capacity performance in the actual construction process, which can not only consider the influence of the installed part of the structure on the co-ordinate adjustment value of the uninstalled part of the structure but also consider the deformation influence of the subsequent uninstalled structure on the installed structure and can realize the optimal approximation of the final state structure to the geometric configuration of the zero state structure and the accurate calculation of the bearing capacity performance with the progress of the construction process.

New EAU/ASCO guideline recommendations on sentinel node biopsy for penile cancer and remaining challenges from a nuclear medicine perspective.

The European Association of Urology (EAU) and the American Society of Clinical Oncology (ASCO) recently issued updated guidelines on penile cancer, emphasising dynamic sentinel node biopsy (DSNB) as the preferred method for surgical staging among patients with invasive penile tumours and no palpable inguinal lymphadenopathy. This paper outlines the rationale behind this new recommendation and describes remaining challenges, as well as strategies for promoting DSNB worldwide. DSNB offers high diagnostic accuracy with the lowest postoperative complications compared to open or minimally invasive inguinal lymph node dissection (ILND), prompting its preference in the new guidelines. Nevertheless, despite its advantages, there are challenges hampering the widespread adoption of DSNB. This includes the false-negative rate associated with DSNB and the potential negative impact on patient outcome. To address this issue, improvements should be made in several areas, including refining the timing and interpretation of the lymphoscintigraphy and the single photon emission computed tomography/computed tomography images. In addition, the quantity of tracer employed and choice of the injection site for the radiopharmaceutical should be optimised. Finally, limiting the removal of nodes without tracer activity during surgery may help minimise complication rates. Over the years, DSNB has evolved significantly, related to the dedicated efforts and innovations in nuclear medicine and subsequent clinical studies validating its efficacy. It is now strongly recommended for surgical staging among selected penile cancer patients. To optimise DSNB further, multidisciplinary collaborative research is required to improve SN identification for better diagnostic accuracy and fewer complications.

Observer-based aperiodically intermittent pinning synchronization of complex-valued dynamical networks with time-varying delay

This paper addresses the impact of time delay and unavailability of system states on synchronization in complex networks. Specifically, it focuses on observer-based synchronization of complex-valued dynamical networks (CDNs) with time-varying delays, encompassing both complex-valued node dynamics and outer coupling forms. Two aperiodically intermittent control schemes, with and without pins, are proposed to reduce control costs by minimizing control frequency and the number of controllers. The proposed schemes establish sufficient criteria for guaranteeing synchronization in CDNs with time-varying delays, utilizing a combination of Lyapunov function and inequality technology in the complex field. Numerical examples are provided to illustrate the effectiveness of the proposed theoretical results.

A self-adaptive attraction and repulsion-based naked mole-rat algorithm for energy-efficient mobile wireless sensor networks

Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor (λ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\lambda )$$\\end{document} is made self-adaptive using simulated annealing (sa) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs.

Learning dynamic embeddings for temporal attributed networks

Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.

Exploring Josephus Interconnection Networks : Unveiling Architecture, Applications, and Recent Advancements

This abstract delves into the distinctive features, applications, challenges, and future directions of Josephus Interconnection Networks. These networks, characterized by a circular topology and deterministic communication paths, present a novel approach to interconnected systems, promising advancements in parallel computing, routing algorithms, and fault tolerance. The recapitulation of key findings highlights ongoing developments in dynamic node management, routing algorithm optimization, and adaptive fault tolerance mechanisms. Implications for interconnected systems encompass efficient parallel processing, rendering Josephus networks apt for distributed and edge computing. The challenges in implementation, notably hardware complexity, underscore the necessity for continual research. Future applications envision roles in distributed computing, edge computing, and potential integration with quantum computing. The abstract concludes by emphasizing the exciting prospects for Josephus networks as they navigate historical inspiration and modern computational challenges in the evolving landscape of interconnected systems.

Toward Stabilizable Large-Scale Boolean Networks by Controlling the Minimal Set of Nodes

This paper studies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structural</i> controllers and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed</i> pinning controllers for the global stabilization of Boolean networks (BNs) by integrating the information on their network structures with nodal dynamics. The main contribution is that several computationally efficient procedures are presented to reduce the number of controlled nodes and to determine a minimal set of controlled nodes without using the brute-force searches. The primary objective is to identify a minimal set of nodes that need to be controlled in the structural controllers for the strong structural stabilization of BNs when network structures is available yet nodal dynamics are unknown. To this end, a theorem shows this minimum controlled node problem can be addressed by seeking the minimum feedback vertex set of network structure. The subsequent part concentrates on designing distributed pinning controllers that merely rely on the node-to-node information exchanges for the global stabilization of BNs with the full knowledge of the nodal dynamics. Several sufficient conditions are provided by utilizing the irreducibility and activation-inhibition network structures to reduce the conservatism. Notably, we claim that, for regulatory BNs without positive cycles, the minimal set of pinned nodes can be determined with a linear amount of time subject to the total number of logical operators in the nodal dynamics. Finally, the effectiveness of these results are validated by three case studies.

Platoon Control of Connected Vehicles from a Networked Control Perspective: Literature Review, Component Modeling, and Controller Synthesis

The platooning of connected and automated vehicles has the potential to significantly benefit the road traffic, including enhancing highway safety, improving traffic capacity, and reducing fuel consumption. This paper presents a four-component analysis framework for platoon systems from a networked control perspective, including a literature review by network awareness, unified models of key components, and two application cases for controller synthesis. The networked control perspective naturally decomposes a platoon into four interrelated components, namely, 1) node dynamics (ND), 2) information flow topology (IFT), 3) formation geometry (FG), and 4) distributed controller (DC). The existing literature is categorized under this framework and analyzed according to the component features. The unified mathematical models are derived for platoons with linear dynamics and distributed controllers. As a case study, a distributed controller synthesis method is introduced for homogeneous platoons, which guarantees the internal stability in the presence of a broad class of topologies with/without uniform time-delays. The effectiveness is demonstrated by numerical simulations.

A product requirement influence analysis method based on multilayer dynamic heterogeneous networks

With the promotion of emerging technologies in the era of big data, there has been a diversification in user participation in product requirement development. This has resulted in many complex, diverse, and rapidly iterated user requirements, greatly reducing the applicability of traditional requirement development methods. In this paper, to address the complex and ever-changing relationships among users, requirements, products, and requirement development teams in the product development process, a multilayer dynamic heterogeneous network node influence analysis method is constructed for product requirement development. This method establishes a product requirement development framework based on a multilayer dynamic heterogeneous network consisting of a user layer, requirement layer, component layer, and expert layer in terms of three dimensions: requirement development, requirement evaluation, and time. Based on this framework, indicators such as comprehensive node significance, layer significance, and node influence are designed. To address the issues encountered in small sample data testing, three measurement models are further designed in this paper, effectively improving the sampling efficiency of the multilayer dynamic heterogeneous network. Additionally, calculation models for the comprehensive node influence and comprehensive node influence variation rate are improved and constructed, and corresponding algorithms are designed. This method is applied in an experimental study of product requirement development for new energy vehicles, providing high-quality recommendations for new energy vehicle requirement developers, including functional requirements and product components that need close and timely attention. Moreover, accuracy tests show that the proposed algorithm achieves an average accuracy rate of 0.8611, which is significantly higher than those of other network node centrality measurement methods. This demonstrates the advanced nature and applicability of the proposed method in the field of product requirement development, providing a new way for requirement developers to improve work efficiency.

A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks

A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks