Dynamic Node Research Articles

Dynamic node influence tracking based influence maximization on dynamic social networks

Influence maximization is one of the popular problems in social network analysis. Its application includes viral marketing, epidemic control, and recommender systems. Most of the existing methods are applicable to static networks. However, many real-world networks are dynamic and they evolve with time. This paper studies influence maximization on dynamic social networks and proposes Dynamic Influence based Seed Selection (DYISSE) method. To find ‘k’ most influential nodes, the proposed method first estimates the influence of each node by introducing a Two-hop Triangular Influence (TTI) that measures the influence strength of each node by utilizing the property of triangles. Based on the TTI, a method named the Dynamic Tractable Set (D-TeSt) is proposed to track the changes in the influence of individual nodes when the topology of the network changes with time. The performance of the DYISSE method is analyzed under the LAIC model on two real-world and twelve synthetic dynamic networks. The results show that the proposed method performs better than the temporal versions of DegreeDiscount, MaxDegree, K-shell, Random, and Closeness centrality measures in extracting initial influential nodes.

An optimized routing scheme for congestion avoidance using mobile nodes in Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of dynamic sensor nodes such nodes form a volatile topology. When an event occurs the dynamic nodes WSN communicate wirelessly from source to destination node without any infrastructure. Dynamic sensor nodes are vital characteristics of WSN where link breakage occurs recurrently and change in topology. Due to this overall network Quality of Service (QoS) degrades due to link failure. Whenever an event in the network experience packet loss due to link failure which leads one of the causes for congestion. However predictable network tries to discover alternate path whenever the link failure occurs on demand which does not have the overall network information thus increasing the communication overhead, delay, and high energy consumption. To address these issues this problem paper proposes an optimized routing scheme known as low latency optimal link-state routing (LL-OLSR) for mobile in WSN which proactively monitors the established path and connects the source to destination with minimum cost with optimal energy. LL-OLSR algorithm finds the disjoint link where it is detected between two nodes and also maximizes the packet delivery ratio by minimizing path delay using connectivity range and queuing delay by which congestion is avoided. Simulation experiment of proposed LL-OLSR scheme is carried on event-driven simulation tool –NS2 and QoS parameters are evaluated by comparing with existing routing scheme.

Universal framework for reconstructing complex networks and node dynamics from discrete or continuous dynamics data.

Many dynamical processes of complex systems can be understood as the dynamics of a group of nodes interacting on a given network structure. However, finding such interaction structure and node dynamics from time series of node behaviors is tough. Conventional methods focus on either network structure inference task or dynamics reconstruction problem, very few of them can work well on both. This paper proposes a universal framework for reconstructing network structure and node dynamics at the same time from observed time-series data of nodes. We use a differentiable Bernoulli sampling process to generate a candidate network structure, and we use neural networks to simulate the node dynamics based on the candidate network. We then adjust all the parameters with a stochastic gradient descent algorithm to maximize the likelihood function defined on the data. The experiments show that our model can recover various network structures and node dynamics at the same time with high accuracy. It can also work well on binary, discrete, and continuous time-series data, and the reconstruction results are robust against noise and missing information.

Efficient reconstruction of directed networks from noisy dynamics using stochastic force inference.

We consider coupled network dynamics under uncorrelated noises that fluctuate about the noise-free long-time asymptotic state. Our goal is to reconstruct the directed network only from the time-series data of the dynamics of the nodes. By using the stochastic force inference method with a simple natural choice of linear polynomial basis, we derive a reconstruction scheme of the connection weights and the noise strength of each node. Explicit simulations for directed and undirected random networks with various node dynamics are carried out to demonstrate the good accuracy and high efficiency of the reconstruction scheme. We further consider the case when only a subset of the network and its node dynamics can be observed, and it is demonstrated that the directed weighted connections among the observed nodes can be easily and faithfully reconstructed. In addition, we propose a scheme to infer the number of hidden nodes and their effects on each observed node. The accuracy of these results is illustrated by simulations.

Explosive synchronization in coupled nonlinear oscillators on multiplex network

We report the emergence of explosive synchronization in a multiplex network where oscillators on the first layer are coupled with attractive coupling and those on the second layer, coupled with repulsive coupling. With Stuart-Landau and FitzHugh-Nagumo oscillators as the nodal dynamics, we consider non-local and mean-field intralayer couplings. We establish that explosive synchronization occurs in the multiplex network in the presence of Gaussian noise, and the transition is first order with hysteresis to a state of complete intra-layer and in-phase interlayer synchronization. The width of the hysteresis depends on the range of intralayer attractive coupling, strength of interlayer coupling and noise. We also see how to have control over this induced transition so that the explosive nature, if undesirable, can be converted to continuous type by tuning the strength of repulsive coupling or noise.

Risk analysis on corrosion of submarine oil and gas pipelines based on hybrid Bayesian network

Submarine pipeline is currently the safest and most economical transportation of subsea oil and gas. Corrosion is one of the main threats to submarine pipelines because of the corrosive of the sea. Therefore, it is significative to evaluate the corrosion risk of the submarine pipelines. In this work, a fault tree model is first employed to identify the corrosion hazards in the transportation process. To overcome the limitations of the fault tree analysis for the states of the events, a Bayesian network (BN) is established through mapping from the fault tree model. Because of the limitations of objective data, experts’ judgment combining fuzzy set theory are used to determine the parameters in the BN. The dynamic nodes in the BN are divided into real-time and discrete nodes, and the dynamic characteristics of them are displayed by the evidence nodes. In this way, when a new observation occurs, the real-time risk analysis of the corroded submarine pipeline is realized. Finally, a case study is carried out to verify the risk level variation with the observing events at different moments. Results shows that the proposed model can dynamically characterize the corrosion risk of the submarine pipeline and provide suggestions for the risk reduction.

Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network.

Distributed Adaptive Mittag–Leffler Formation Control for Second-Order Fractional Multi-Agent Systems via Event-Triggered Control Strategy

This brief investigates the Mittag–Leffler formation bounded control problem for second-order fractional multi-agent systems (FMASs), where the dynamical nodes of followers are modeled to satisfy quadratic (QUAD) condition. Firstly, under the undirected communication topology, for the considered second-order nonlinear FMASs, a distributed event-triggered control scheme (ETCS) is designed to realize the global Mittag–Leffler bounded formation control goal. Secondly, by introducing adaptive weights into triggering condition and control protocol, an adaptive event-triggered formation protocol is presented to achieve the global Mittag–Leffler bounded formation. Thirdly, a five-step algorithm is provided to describe protocol execution steps. Finally, two simulation examples are given to verify the effectiveness of the proposed schemes.

Structural Equilibrium Control of Network Systems

During the analysis and design of a network system, a frequently encountered situation is when the network structure is <i>known</i> but the node dynamics is practically <i>unknown</i>. Thus, structural analysis in which the prior knowledge is mostly about the network structure has received attention in recent years. Motivated by this line of research, this article focuses on a structural design problem called the <i>structural equilibrium control problem</i>, which is used to find a sign pattern in the control input such that any constant input with the sign pattern drives the system to a desirable steady state in a qualitative sense. A solution is presented by reducing it to a design problem of the so-called <i>sign-solvable equation</i>, which is a linear equation whose qualitative solution can be determined from the prior knowledge of the sign pattern of the coefficients.

Markov Chains for Fault-Tolerance Modeling of Stochastic Networks

Most real-world networks are time-varying, and many are subject to the stochastic functioning of their nodes and edges. Examples can be seen in the human brain undergoing an epileptic seizure, spontaneous infection and recovery in epidemics, and intermittent functioning of devices in the Internet of Things. Moreover, such networks are becoming increasingly large due to rapid technological advances. However, little has been done to study time-varying, large-scale, stochastic networks (SNs) from a reliability engineering perspective. Toward this goal, this article develops a fault-tolerance model for a type of time-varying network in which nodes (and/or edges) stochastically switch between active and inactive states. It considers fault tolerance from a global connectivity point of view, which has applications in many natural and engineered networks. Specifically, this article presents a Markov chain framework that models the dynamic behavior of nodes and allows for the computation of quantitative measures, including availability and time-to-failure metrics. To accommodate large-scale networks and emphasize global connectivity, this framework utilizes percolation theory, which has recently been of interest in the reliability engineering discipline, to characterize network failure. This article makes several contributions: it proposes a Markov chain framework for computing fault-tolerance metrics that is tractable for large-scale networks, it shows the existence of a phase transition in network availability of a time-varying SN, and it accounts for finite-size effects of percolation in the fault-tolerance model. The proposed methodology is applied to Erdös–Rényi random graphs and a real, large-scale power grid. Experimental results provide insights into network design, maintenance, and failure prevention of time-varying SNs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work develops a fault-tolerance model for time-varying stochastic networks in which nodes (and/or edges) randomly switch between active and inactive states. To address increasingly large-scale networks that are being studied, this article appeals to percolation theory. Fault tolerance is, thus, studied from a global connectivity perspective where the existence of a large connected component containing most of the nodes characterizes the functioning of the network. Specifically, this article presents a continuous-time Markov chain (CTMC) framework that models node dynamics and allows for the computation of fault-tolerance metrics, including network availability and mean time to failure. The proposed framework computes metrics efficiently for large networks and allows for studying their asymptotics. The percolation threshold describing the dissolution of the large connected component is used as the failure criterion in the CTMC. The practitioner should note that several assumptions are made in the proposed CTMC framework: nodes possess identical and time-invariant failure and recovery rates, node failure and recovery times are exponentially distributed, and node dynamics are independent of one another. In addition, fault-tolerance metrics computed for finite networks are estimators of the true metric values. The proposed framework is advantageous for quantifying the fault tolerance of large-scale, time-varying networks where the combinatorial explosion and a changing network topology pose challenges to the use of traditional reliability methods. A case study of a power grid network shows how to apply the proposed methodology to real networks.

Output-Feedback Self-Synchronization of Directed Lur'e Networks via Global Connectivity.

In this article, we generalize the results on self-synchronization of Lur'e networks diffusively interconnected through dynamic relative output-feedback from the undirected graph case in Zhang et al. 2016 to the general directed graph case. A linear dynamic self-synchronization protocol of the same structure is adopted as the one proposed in Zhang et al. 2016. That is, the Lur'e-type nonlinearity is not involved in our self-synchronization protocol. It is in fact unknown and only assumed to be incrementally sector bounded within a given sector. In the absence of a leader Lur'e system defining the synchronization trajectory, we construct a novel self-synchronization manifold in order to derive the self-synchronization error dynamics. Meanwhile, the connectivity of the general directed graph having a directed spanning tree is quantified by the global connectivity, instead of the so-called general algebraic connectivity used in the directed graph case under static relative state feedback. The global connectivity plays a crucial role in handling self-synchronization problems of directed nonlinear networks via dynamic relative output feedback, including directed networks with the Lipschitz nonlinear node dynamics, which is also discussed in this article. The protocol parameter matrix design is performed by solving the obtained LMI conditions in sequence. In addition, some discussions are complemented on the important technical details in our self-synchronization protocol design along with extensions. Finally, our theoretical results are illustrated through numerical simulations over a directed nonlinear dynamical network.

Low Leakage Low Power Domino Logic Technique for Wide Fan-In Applications, 40-Bit Tag Comparator

Advances in sub-micron technologies make low power consumption and delay a major concern for present day systems. These parameters play a critical role in the performance of most widely used wide fan-in-dynamic logic gates. These wide fan-in dynamic gatesare employed in designing high speed tag comparators which are critical blocks of cache memory. The Stack-Scheme Clocked-Bleeder Domino (SS-CBD)tries to have an insight on a 40-bit tag comparator in terms of power consumption and noise immunity to produce a high-performancelogic style. The approach limits the voltage swing at the dynamic node with the helpof stack transistor employed between dynamic node and clocked bleeder transistor. This technique will reduce the overall power consumption and improves the gate speed for constant noise immunity. The observation is carried out with 1 GHz clock frequency and 0.9 V supply at 27 0C temperature using Cadence Virtuoso Spectre and Layout editor for GPDK 90 nm CMOS technology.

Implementasi Algoritma Pemilihan Node Tetangga Terbaik Pada Protokol Routing DSR di Jaringan MANET

Mobile Ad Hoc Network (MANET) is a wireless telecommunications technology that consists of a collection of dynamic nodes. Due to these properties resulted in the mobility of nodes in MANET. So that every relationship between nodes will always change and cause the process of finding a vulnerable route to take a long time. Each node also has a role as a host or router in exchanging information. In MANET delivery assurance and the ability to deal with path changes on an ad hoc basis are critical. So that in sending data packets from the source node to the destination node, a Routing protocol is needed. The routing protocol Dynamic Source Routing (DSR) in the route search process broadcasts RREQ to all neighboring nodes (intermediate nodes) without knowing whether the node is the best node or not. So that the possibility of the route being interrupted because it is not composed of the best nodes can occur. A modification of the DSR routing protocol is proposed by finding the best intermediate node based on parameters, namely bandwidth, RTT, packet loss ratio. Then the neighboring nodes (intermediate nodes) that have been selected will arrange the delivery route from the source node to the destination node. The performance of the modified DSR and DSR routing protocols will then be measured using test parameters, namely throughput, packet delivery ratio (PDR) and average end to end delay. Based on the results of research trials, the application of the modified DSR method can improve the performance of the DSR protocol. Which after the application of the method, the average throughput increased by 25%, Packet Delivery Ratio (PDR) was 1.11% and the average end-to-end delay decreased by 17.64 %.

Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes

Due to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli–Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1–2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches.

Crowd Flow Prediction for Irregular Regions with Semantic Graph Attention Network

It is essential to predict crowd flow precisely in a city, which is practically partitioned into irregular regions based on road networks and functionality. However, prior works mainly focus on grid-based crowd flow prediction, where a city is divided into many regular grids. Although Convolutional Neural Netwok (CNN) is powerful to capture spatial dependence from grid-based Euclidean data, it fails to tackle non-Euclidean data, which reflect the correlations among irregular regions. Besides, prior works fail to jointly capture the hierarchical spatio-temporal dependence from both regular and irregular regions. Finally, the correlations among regions are time-varying and functionality-related. However, the combination of dynamic and semantic attributes of regions are ignored by related works. To address the above challenges, in this article, we propose a novel model to tackle the flow prediction task for irregular regions. First, we employ CNN and Graph Neural Network (GNN) to capture micro and macro spatial dependence among grid-based regions and irregular regions, respectively. Further, we think highly of the dynamic inter-region correlations and propose a location-aware and time-aware graph attention mechanism named Semantic Graph Attention Network (Semantic-GAT), based on dynamic node attribute embedding and multi-view graph reconstruction. Extensive experimental results based on two real-life datasets demonstrate that our model outperforms 10 baselines by reducing the prediction error around 8%.

Role of assortativity in predicting burst synchronization using echo state network.

In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in the machine's prediction. However, in the case of assortative network, training the machine with the information (i.e., time series) of low degree nodes gives better results in predicting the burst synchronization. The results are found to be consistent with the investigation carried out with a continuous time Hindmarsh-Rose neuron model. Furthermore, the role of hyperparameters like spectral radius and leaking parameter of ESN on the prediction process has been examined. Finally, we explain the underlying mechanism responsible for observing these differences in the prediction in a degree correlated network.

An Improvement Heuristic Based on Variable Neighborhood Search for Dynamic Orienteering Problems with Changing Node Values and Changing Budgets

We study the Dynamic Orienteering Problem (DOP) with changing node values and changing budgets. It is a complex combinatorial optimization problem with many applications, e.g., in tour planning. To solve the DOP, an improvement heuristic based on Variable Neighborhood Search mathrm {VNS}_{mathrm {DOP}} is proposed. In addition, three methods for handling solutions that became invalid by budget changes are presented. Heuristic mathrm {VNS}_{mathrm {DOP}} is experimentally compared with two improvement heuristics based on state-of-the-art algorithms for the static Orienteering Problem. In addition, the influence of the three invalid solution handling methods on the algorithms’ optimization behavior is evaluated experimentally. For the experiments, benchmark instances as well as instances generated from existing road networks are used. As a quality measure for the algorithms, their performance over time is used. The results show that both types of dynamic changes, i.e., changes in the node values and changes in the budget, lead to higher volatility in the results for all compared algorithms. However, the latter type has a more negative effect on the performance. Out of the compared algorithms, the proposed heuristic mathrm {VNS}_{mathrm {DOP}} obtains the best results in most cases on a variety of problem instances with dynamic node values and dynamic budgets, showing that it has a high performance over time in dynamic environments and is able to deal with different levels of dynamic changes. For DOPs with changing budgets, the invalid solution handling method that repairs solutions by fixing the violation of the budget constraint as fast as possible performs best for the considered algorithms.

The stationarity control of the average links for the Hebb complex dynamical network via external stimulus signals

The model of complex dynamical network (CDN) can be represented as the mathematic graph, in which some characteristics may emerge from the dynamic nodes group (NG) and links group (LG). This paper primarily focuses on the feature appearing from the dynamic links. The average link weight (ALW), as a novel quantitative index to describe the characteristic of dynamic links is introduced. Inspired by the Hebb’s neuroscience theory, the Hebb complex dynamical network (HCDN) is constructed. The ALW of the HCDN can track a given target via external stimulus signals with adaptive amplifiers’ proportional coefficients. In other words, the stationary network implies the ALW is a constant in time. Finally, two simulation examples are performed to validate the proposed adaptive update law’s effectiveness.

Mobility Models as a Key-Performance Factor for Wireless Networks

Nodes in wireless networks can be static or dynamic. Static nodes are stationary and their positions are fixed and their distribution may follow a particular topology. On the other hand, dynamic nodes are considered mobile and their positions changed over time due to following a specific pattern of movement, which also lead to the concept of the topology being irrelated. Simulating such networks needs a lot of attention and many requirements should be held. For instance, nodes in dynamic wireless networks are distributed according to a distribution that reflects the nature of the environment (e.g., Gaussian, Power-Law, Uniform distribution, etc.). Moreover, describing the movement patterns of nodes in dynamic wireless networks need to follow a particular mobility model that describes the movements of nodes in terms of speed and direction (e.g., Individual mobility model, Levy Flight Model, Cauchy Model). In addition to the aforementioned requirements, it is needed to incorporate a routing protocol that governs data spread within the simulation environment. This work designed a variety of experiments that combine the mentioned requirements and measure the performance of dynamic wireless networks under colorful settings and configurations. The metrics used in this approach are the amount of data exchanged, coverage area, and power and memory consumption. Mobility models, node deployment strategy (distributions), and routing protocols are the three factors that will be involved in the experiments. The experiments deal with four mobility models Random Way Point Mobility Model (RWPM), Street Random Way Point Mobility Model (SRWPM), Manhattan Mobility Model (MM), and Levy Flight Mobility Model (LFM). Three distribution models will be used in the experiments Power-Law Distribution, Chi-Squared Distribution, and Normal Distribution. The third factor that will be involved in the routing protocol, is Probabilistic Flooding Routing Protocol. Different experiments are benchmarked using these metrics. The results of this work will reveal facts about dynamic network simulations and provide some recommendations to network developers and architects.

Graph neural network and multi-agent reinforcement learning for machine-process-system integrated control to optimize production yield

In this paper, an integrated control framework is proposed for the optimization of the production yield by integrating different levels of a manufacturing system, including system, process, and machine levels. The manufacturing system is modeled as a graph by treating machines as nodes and material flows as links. The graph model enjoys high flexibility and is able to incorporate all relevant real-time information across all levels of the manufacturing system in the dynamic node feature. Since the real-time tool state is essential for decision making, Recursive Bayesian Estimation (RBE) is adopted to reduce the tool state observations through sensors and machine learning models and provide more accurate tool state estimation to be included into the graph node feature. With the graph model, Graph Neural Network (GNN) is applied to process the node features to generate node embedding that reflects both local and global information. For the integrated control purpose, each machine node is then be treated as a distributed agent in Multi-Agent Reinforcement Learning (MARL) that conditions its policy on the node embedding from GNN. State-of-the-art GNN and MARL algorithms, namely Graph Attention Network (GAT) and Value Decomposition Actor Critic (VDAC), are implemented to train learnable parameters in GNN-MARL networks to learn the optimal multi-agent policy. Extensive numerical experiments and analysis proves the effectiveness of the proposed integrated control framework.