Many studies assume that individuals are perfectly rational or only update vaccination strategies at the end of each transmission season, without considering differences in individual perceptions and behaviors. This article proposes a novel coevolutionary model that integrates subjective perceptions with social interactions, where individuals update vaccination strategies within the same transmission season. The coevolutionary model effectively controls epidemic spread within a single transmission season and uncovers nonlinear effects of the factors overlooked by the traditional static synchronous update (SSU) model. We examine how individual decisions evolve in response to a changing epidemic environment and employ a two-layer coupled network to study the interplay between information and epidemic transmission. Our results indicate that higher contact strengths reduce the epidemic threshold, indicating that contact strengths promote the spread of the epidemic in the early stages. As the epidemic progresses, however, the coevolutionary feedback mechanism, driven by individuals’ responses to infection risks, encourages vaccination, thereby controlling epidemic transmission. Unlike traditional conclusions, higher vaccination costs do not necessarily lead to a smaller vaccination equilibrium. The reason may be that higher costs enhance individuals’ aversion to infection risks. In addition, our analysis also reveals that changes in vaccine efficacy duration may be ineffective in reducing infection densities in early outbreaks. However, under the impact of coevolution, the vaccination equilibrium decreases with the increase of the short vaccine efficacy duration, while it increases with the long vaccine efficacy duration.

This article focuses on the constrained maximal controllability of complex networks, which aims to maximize the generic dimension of controllable subspace of networks with a given candidate set of constrained input locations. To address this issue, we first transform it to a maximum general-cactus cover problem. By introducing network flow, this problem is further converted to a minimum-cost maximum-flow problem. An algorithm named minimum-cost maximum-flow-based general-cactus cover (MMGC) is proposed to achieve the optimal solution. Furthermore, a series of simulations on Erdős-Rényi networks (ERNs) and scale-free networks (SFNs) and applications in network controllability robustness demonstrates the effectiveness of MMGC. The simulation results have revealed that augmenting the number or range of inputs can enhance the controllability of networks, and the presence of multicyclic structures significantly strengthens the controllability robustness of complex networks.

Complex strategic interactions of rational agents are ubiquitous in decision-making groups which greatly influence the evolutionary dynamics of many real-life networked systems. Here, we study how individual decision-making behaviors evolve when the topology of network interactions is weighted, and how the network of mixed coordinating and anti-coordinating games is driven to an equilibrium. We prove that the weighted pure coordinating or anti-coordinating decision-making dynamics, under both asynchronous and partially synchronous updates, will converge to the Nash equilibrium after finite strategy switches. Moreover, it follows that the upper bound on the number of switches for the convergence depends on the number of agents and the weights' distribution under asynchronous update. For mixed coordinating and anti-coordinating games, we find that network game dynamics can be decoupled into convergence and nonconvergence regions under certain conditions, in which the global convergence can be established by adding leaf vertices. For more general cases, we devise the incentive mechanisms for agents to achieve the convergence. We also extend the incentive performance of fully asynchronous updating to the partially synchronous updating. Our results provide hints on the typology and incentive mechanisms to induce the convergence of mixed gaming networks.

Multielement decision-making is crucial for the robust deployment of human-robot collaboration (HRC) systems in flexible manufacturing environments with personalized tasks and dynamic scenes. Large Language Models (LLMs) have recently demonstrated remarkable reasoning capabilities in various robotic tasks, potentially offering this capability. However, the application of LLMs to actual HRC systems requires the timely and comprehensive capturing of real-scene information. In this study, we suggest incorporating real scene data into LLMs using digital twin (DT) technology and present a cognitive digital twin prototype system of HRC manipulation, known as HRC-CogiDT. Specifically, we initially construct a scene semantic graph encoding the geometric information of entities, spatial relations between entities, actions of humans and robots, and collaborative activities. Subsequently, we devise a prompt that merges scene semantics with prior knowledge of activities, linking the real scene with LLMs. To evaluate performance, we compile an HRC scene understanding dataset and set up a laboratory-level experimental platform. Empirical results indicate that HRC-CogiDT can swiftly perceive scene changes and make high-level decisions based on varying task requirements, such as task planning, anomaly detection, and schedule reasoning. This study provides promising insights for the future applications of LLMs in robotics.Note to Practitioners—Recently, LLMs have demonstrated significant success in various robotic tasks, suggesting their potential as a powerful tool for robotic decision-making. Motivated by this, to improve the production efficiency of HRC in flexible manufacturing, we innovatively combine LLMs with DT technology, and propose a cognitive DT system for HRC, aiming to integrate LLMs into the decision-making loop of HRC system. Experiments conducted in a laboratory-scale platform indicate that the proposed system can handle different decision-making needs in different HRC activities. This system can provide professional guidance to operators in a comprehensible form and serve as a medium for monitoring the safety and standardization of the manipulation process. Future work will explore the use of virtual space provided by the proposed system to optimize the decision outputs of LLMs to make the proposed system more broadly applicable.

This paper investigates the bearing-based cooperative circumnavigation control problems for multi-agent systems to enclose multiple static and moving targets on adaptively designed elliptical orbits. Firstly, a novel method based on estimated relative positions of agents with respect to targets is proposed to adaptively design the elliptical orbits to improve the adaptability of circumnavigation. The relative positions between agents and targets are estimated using only bearing measurements. A bearing-only circumnavigation control law is then proposed to enable a single agent to elliptically circumnavigate multiple targets on the adaptively designed orbit. For a group of agents, the control law for a single agent is extended by incorporating affine formation and collision avoidance. Furthermore, the effectiveness of the proposed orbit design method for enclosing targets and the convergence of the elliptical circumnavigation control laws are analyzed theoretically with their advantages verified by extensive simulations.

Predicting future dynamics on networks is challenging, especially when the complete and accurate network topology is difficult to obtain in real-world scenarios. Moreover, the higher-order interactions among nodes, which have been found in a wide range of systems in recent years, such as the nets connecting multiple modules in circuits, further complicate accurate prediction of dynamics on hypergraphs. In this work, we proposed a two-step method called the topology-agnostic higher-order dynamics prediction (TaHiP) algorithm. The observations of nodal states of the target hypergraph are used to train a surrogate matrix, which is then employed in the dynamical equation to predict future nodal states in the same hypergraph, given the initial nodal states. TaHiP outperforms three latest Transformer-based prediction models in different real-world hypergraphs. Furthermore, experiments in synthetic and real-world hypergraphs show that the prediction error of the TaHiP algorithm increases with mean hyperedge size of the hypergraph, and could be reduced if the hyperedge size distribution of the hypergraph is known.

The spread of epidemics is a complex process influenced by multiple social relations, including families, schools, and companies. Group interactions represented by a simplicial complex have been shown to impact the dynamics of epidemic transmission significantly. However, the previously proposed higher order contagion models only consider the propagation process within the higher order structure but lack a description of the consistency of the node states in the structure. To address this problem, we provide solutions to create a solvable network model that shows network clusters and node state consistency tendencies, capable of capturing the coexistence of interacting groups. We analyze the bistable region and epidemic threshold of the higher order propagation dynamics and explore the effect of the state update of the nodes on the model in: 1) random regular simplicial networks; 2) triangular simplex-lattice networks; 3) star-simplicial networks; and 4) heterogeneous simplicial networks. Our theoretical analysis, complemented by Monte Carlo numerical simulations, demonstrates that the proposed model accurately captures phase transitions and the bistable region created by higher order interactions. In addition, we find that the incorporation of intersimplex interaction mechanisms greatly reduces the epidemic threshold of the system and produces a larger bistable region, in which the dynamics of the system are heavily affected by the density and location of the initially infected nodes. Our findings contribute to a better understanding of higher order interactions in complex networked systems.

In this paper, we propose a new criteria to measure network controllability robustness based on generic dimension of controllable subspace given a network. Generic dimension curve and controllabillity robustness index are introduced to show network controllability robustness from different aspects. 12 different attack methods are carried out for six network models. It is found that the multiloop structure is useful to maintain network robustness under attacks. Moreover, the networks with single backbone structure are fragile under attacks. Besides, both the increase and protection of driver nodes can enhance the controllability robustness of networks.

The prediction of future dynamics on networks is a challenge. Unfortunately, due to the noise in the sampling process, or the low resolution of observational data, it is hardly feasible to get the complete topology of real-world networks. Moreover, the higher-order interactions among nodes add the difficulty to accurate prediction of dynamics on networks. In this work, we proposed a two-step approach for higher-order dynamics prediction without network topology. First, the observations of nodal dynamics of a specific dynamical model on unknown hypergraphs are collected to solve an optimization problem by Ridge regression, to obtain a surrogate incidence matrix. Second, the prediction is obtained by iterating the equation of the dynamical model with the surrogate incidence matrix. We define the average relative prediction error to evaluate the performance of our prediction method, and a wide range of hypergraph dynamics with different parameters are predicted. The prediction accuracy is positively correlated with the number of hyperedges the hypergraph contains and the contact rate in the dynamical model.

Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks. Traditionally, network robustness is determined by attack simulations, which is very time-consuming and even incapable for large-scale networks. Network Robustness Learning, which is dedicated to learning network robustness with high precision and high speed, provides a powerful tool to analyze network robustness by replacing simulations. In this paper, a novel versatile and unified robustness learning approach based on the customized graph transformer (NRL-GT) is proposed, which consists of a particularly designed backbone and three branches to accomplish the tasks of robustness curve learning, overall robustness learning and synthetic network classification simultaneously. Numerous experiments show that: 1) NRL-GT is a unified learning framework for controllability robustness and connectivity robustness, demonstrating a strong generalization ability to ensure high precision when training and test sets are distributed differently; 2) It is theoretically and experimentally demonstrated the proposed graph transformer layer is capable of outperforming classical graph neural networks. Compared to the cutting-edge methods in network robustness learning, NRL-GT can simultaneously perform network robustness learning from multiple aspects and obtains superior results in less time on both synthetic and real-world networks, especially circuit networks and power networks; 3) It is worth mentioning that with the transferability of the proposed backbone, NRL-GT is able to deal with complex networks of different sizes for different tasks efficiently and effectively.

Previous studies have focused on the assumption that individual attitudes toward epidemics, epidemic-related information, and vaccination are identical with much less effort attempting to consider psychological factors and individual heterogeneity. In this article, we employ a two-layer susceptible–infected–recovered–vaccinated/unaware–aware–unaware (SIRV-UAU) coupled network to depict the interplay between epidemic spreading and information diffusion. We propose an imperfect vaccination evolutionary game model integrating prospect theory (PT) to explore the effect of individual subjective perception and social difference on epidemic spreading and vaccination equilibrium. We find that the individual social difference in the epidemic spreading layer has a more significant impact on the epidemic threshold than that in the information diffusion layer. Individual psychological perception and vaccination behavior decision-making determine the vaccination equilibrium: under the PT, vaccination equilibrium increases with the decrease of the rationality coefficient when the vaccination cost is small. Furthermore, we analyze the effect of social differences on individual vaccination behavior decision-making and find that vaccination equilibrium increases with the increase of social reinforcement strength and primary protective probability. Finally, we discuss the effect of infection cost on vaccination equilibrium when the infection cost of vaccinated individuals is less than that of unvaccinated individuals and observe that a small infection cost helps improve the vaccination equilibrium.

This article studies the tracking control problem of an autonomous quadruped robot (AQR). A new kinematic model is presented to describe both the translation mode and the rotation mode of the AQR using the same number of inputs as compared with the linear inverted pendulum model. We newly propose a sequence-scaling Lyapunov-based model predictive control algorithm for the AQR to improve the tracking performance. Within the control framework, the sequence-scaling strategy is introduced to optimize the tracking, and simultaneously guarantee the closed-loop stability. The practical constraints, such as dynamic balance of motion and speed limits, are explicitly considered to provide a feasible tracking trajectory to the AQR. We analytically address the closed-loop stability, where a contraction constraint is built within the gait sequence. Simulation and hardware experiments on Unitree Aliengo witness the superior real-time control performance via the proposed algorithm.

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely one CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability for both cases of known and unknown datasets, with significantly lower time-consumption, than its counterparts.

Since human face-to-face communications change over time, their perceived probabilities associated with uncertain outcomes usually present a nonlinear fashion in reality. This article incorporates both humans active characteristics and risk perception into epidemic spreading and explores a comprehensive evolutionary vaccination game in activity-driven networks. According to the weighting effect, the individuals’ activity rate thresholds are obtained in different cases under the framework of the pure Nash equilibrium. Furthermore, how the number of connected edges of activated individuals$m$and infection rate$\lambda $affect individuals’ vaccination, decision-making is theoretically analyzed. It is proven that some unvaccinated individuals gradually become vaccinated individuals with the increase of$m$or$\lambda $. Moreover, there is a unique pure Nash equilibrium social state (Pareto optimization), and the pure Nash equilibrium together with Pareto optimization has the same equilibrium for our proposed evolutionary vaccination game, which provides an alternative condition judging the stability of game system. In the case of complete information and individual complete rationality, the factors, including vaccination cost, individuals’ activity rate, the number of connected edges of activated individuals, and infection rate, have a significant influence on the final average social cost.

Network robustness is critical for various societal and industrial networks against malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible for large-scale networks. In this article, an improved method for network robustness prediction is developed based on learning feature representation using the convolutional neural network (LFR-CNN). In this scheme, the higher-dimensional network data are compressed into lower-dimensional representations, which are then passed to a convolutional neural network to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that: 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly smaller prediction errors; 2) LFR-CNN is insensitive to the variation of the input network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; and 4) LFR-CNN not only accurately predicts the network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.

In this article, the consensus of constrained linear heterogeneous multiagent systems under prediction and optimization is investigated. By optimizing the consensus problems constrained to state equations and general linear constraints, two types of distributed analytical model predictive controllers are proposed. Furthermore, stability conditions for the two types of controllers are derived, in which a relationship between the communication topology and dynamics of heterogeneous agents is clarified. Simulation examples of networked heterogeneous agents illustrate the convergence and validity of the proposed controllers.

Privacy concerns have long been a nuisance for users when using online social networks (OSNs), and most OSNs require users to configure the privacy settings themselves. Researchers are paying attention to the recommendation of privacy settings for OSN users, as some studies have found it difficult for users to understand and to use. This paper addresses the problem of recommending user-level privacy settings by leveraging the user-generated content (UGC) sequences and account information. We first analyze the differences of users with different privacy settings using the crawled Twitter dataset. Further, we formalize the privacy settings recommendation problem as a multi-label classification task and propose a deep learning-based privacy settings recommendation approach, PrivacyRec. It adopts Recurrent Neural Networks (RNNs) for the UGC sequences modeling to extract deep representations of users' semantic preferences and interaction behavior. In addition, it fuses the above representations with processed account portrait feature through attention mechanism and infers the privacy settings. We evaluate PrivacyRec using two real-world datasets and compare it with several baseline methods. The experimental results demonstrate the effectiveness of PrivacyRec's ability to recommend privacy settings.

This paper focuses on the spatial-spectral terahertz (THz) networks, where transmitters equipped with leaky-wave antennas send information to their receivers at the THz frequency bands. As a directional and nearly planar antenna, the leaky-wave antenna allows for information transmissions with narrow beams and high antenna gains. The conventional large antenna arrays are confronted with challenging issues such as scaling limits and path discovery in the THz frequencies. Therefore, this work exploits the potential of leaky-wave antennas in the dense THz networks, to establish low-complexity THz links. By addressing the propagation angle-frequency coupling effects, the transmission rate is analyzed. The results show that the leaky-wave antenna is efficient for achieving the high-speed transmission rate. The co-channel interference management is unnecessary when the THz transmitters with large subchannel bandwidths are not extremely dense. A simple subchannel allocation solution is proposed, which enhances the transmission rate compared with the same number of subchannels with the equal allocation of the frequency band. After subchannel allocation, a low-complexity power allocation method is proposed to improve the energy efficiency.

The vertex cover of networks is a classical combinatorial optimization problem. In this paper, we investigate the vertex cover problem solved by the memory-based best response update rule, which can not converge to a strict Nash equilibrium (SNE) with memory length m =3D 1. To overcome this shortcoming, a bounded rational behavioral (BRB) update rule is newly proposed in this paper. We prove that the BRB with m =3D 1 can guarantee that the whole vertices' state converges to a SNE. The simulation is carried out to verify that the performance of the proposed BRB update rule on representative networks. Moreover, we also find that a better SNE will be achieved by increasing the selection intensity.

This article explores the global stochastic synchronization of the Kuramoto-oscillator networks with duplex topological structures. The initial phase diameter can be arbitrarily large and the coupling gain of the Kuramoto-oscillator networks can be relatively weak. In particular, two different scenarios of the noise diffusion process are introduced, which cover the noise affecting the sinusoidal coupling process in the Kuramoto-oscillator layer and the networked communication in the control layer, respectively. The local and global connectivity criteria, related to the network topologies, coupling strength, and control gains, are derived rigorously to achieve the global stochastic asymptotic phase agreement and frequency synchronization, respectively. Finally, the validity of the theoretical results is verified via numerical simulation, which further shows that phase agreement is robust to noise perturbation, while frequency synchronization is peculiarly sensitive.

Being a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this article, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found several unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected component, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users and then look into the tip contents from the perspectives of tip venues, temporal patterns, and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to the profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users and are constructive for different relevant entities, including LBSN service providers, Internet service providers, and third-party application providers.

Vaccination is one of the most effective strategies against epidemics. Traditionally, the evolutionary game theory focuses on individual behaviors, rather than psychological cognition behind which reflects individual risk attitude. How does individual perception impact the collective decision and performance of vaccination? To explore this question, we propose an evolutionary vaccination game model by integrating the prospect theory (PT), which accounts for individual subjective perception under uncertainty. In this article, the uncertainty stems from the interactions of individuals in the potential infection process. After analyzing the evolutionary vaccination dynamics, we compare the vaccination equilibrium under the PT and the expected utility theory (EUT) and highlight the role of perceptions on promoting vaccination. In addition, we find that the performance of different perceptions captured by the weighting effect (WE) and the framing effect in epidemic control depends on the vaccination cost. Specifically, both the fraction of infected individuals and the social cost decrease with the decrease of the rationality coefficient under the WE when the vaccination cost is relatively small. The dependence reveals that risk-averse and loss aversion dominate individual vaccination behavior. The numerical simulations verify the effect of perception and the effectiveness of vaccinating the network epidemics.

Many complex systems can be modeled as temporal networks with time-evolving connections. The influence of their characteristics on epidemic spreading is analyzed in a susceptible-infected-susceptible epidemic model illustrated by the discrete-time Markov chain approach. We develop the analytical epidemic thresholds in terms of the spectral radius of weighted adjacency matrix by averaging temporal networks, e.g., periodic, nonperiodic Markovian networks, and a special nonperiodic non-Markovian network (the link activation network) in time. We discuss the impacts of statistical characteristics, e.g., bursts and duration heterogeneity, as well as time-reversed characteristic on epidemic thresholds. We confirm the tightness of the proposed epidemic thresholds with numerical simulations on seven artificial and empirical temporal networks and show that the epidemic threshold of our theory is more precise than those of previous studies.

The integration of traditional Chinese medicine (TCM) and modern medicine (MM) focuses on the wellness and healing of the whole person, and promises an improved treatment. Yet it is not well known what the correlation between TCM symptoms and MM symptoms is, and how such correlation changes in a certain disease. In this paper, we construct the symptom networks and compare the topology of the networks between the fatty liver disease (FLD) patients group and the healthy controls group, with the physical and TCM examination data. Specifically, we explore the correlation between TCM and MM symptoms. Furthermore, the centrality metrics such as eigenvector centrality, h-degree and c-index, are used to identify the significant symptoms. Moreover, to validate the effectiveness of the identification of significant symptoms, we quantify the ability of these symptoms as biomarkers to distinguish FLD patients and healthy controls. Our results demonstrate that the construction of symptom networks and centrality metrics shed a new light on the analysis of the correlation among symptoms and the recognition of significant symptoms.

This paper investigates the robust distributed model predictive control (DMPC) based consensus problem of general linear discrete-time multi-agent systems subject to external disturbances. We first propose a robust DMPC based consensus algorithm, mainly relying on the DMPC based consensus of a nominal system whose state deviation from the actual state is bounded by an invariant set. We then prove the feasibility and robust consensus of the proposed algorithm. Finally, we present a numerical example to verify the theoretical results.

Macrotasking crowdsourcing systems (MCSs), such as Google Helpouts and Elance have emerged as an effective paradigm for improving human intelligence and activity to solve a wide variety of tasks. Requesters often post tasks to the MCS and competitive workers solve the tasks to earn the reward. However, rational and selfish workers in the MCS aim to strategically maximize their own benefit by exhibiting malicious behaviors, thereby decreasing the efficiency of systems. Herein, we present a novel game-theoretic mechanism to incentivize the competitive and selfish workers to provide high-quality solutions in the MCS. We first formulate the crowdsourcing problem as a multiplayer iterated game with incomplete information, where each worker has certain private information (such as solution quality), but does not know what other workers do. Subsequently, we propose an incentive mechanism in terms of zero-determinant (ZD) strategies aiming to improve the social welfare of the MCS, which serves to incentivize the competitive selfish workers toward high-quality solutions. Moreover, we find the conditions for reaching the maximum social welfare of the MCS. Numerical illustrations demonstrate a high and stable social welfare of the MCS with the proposed ZD strategies mechanism.

This paper investigates the consensus problem of general linear discrete-time multiagent systems by using distributed model predictive control (DMPC) with self-triggered mechanism. First, a novel DMPC-based consensus algorithm is proposed, where each agent only needs to obtain its neighbors' predicted state sequences once at each time step. We prove that the resultant DMPC optimization problem is feasible, and the proposed algorithm guarantees the dynamic consensus of agents. Then, to further reduce the communication cost and the energy consumption of control updates, a self-triggered DMPC-based consensus algorithm is proposed with the control input and the triggering interval jointly optimized. Numerical examples including the benchmark problem with platooning vehicles are provided to verify the effectiveness and advantages of the proposed algorithms.

As the access to high resolution datasets from real physical proximity becomes more and more convenient, plenty of temporal network models have been proposed to model human interaction patterns. However, less work has been devoted to investigating that to what degree the contact network expresses human memory, which is much crucial to forming a realistic network structure. Here we analyze three empirical datasets which reflect temporal human face-to-face interactions. We first quantify the strength of contact memory on the agent level and network level, respectively. We find that human interactions have the short-term memory. Then, we study the difference between the duration of the consecutive interaction events of the same two agents. We find that the contact memory exists in the duration of the individual interaction events.

Zero-determinant strategies provide a novel perspective for revealing the relation between a game strategy and the cost, and play an important role in effectively controlling and optimizing the costs against virus spreading in vaccination games. We explore in this paper how the social cost can be optimized when a self-interested individual adopts the newly proposed admin-cost-based zero-determinant strategy (AZDS) to unilaterally control his own cost. In contrast to the traditional belief of the conflicts between personal interest and public (social) interest, we find that the AZDS is a win-win mechanism which presents the advantage in optimizing both the individual cost and social cost simultaneously. Taking into account the differences between the individuals (nodes) in a network, we investigate the topological influence on the performance of the AZDS implemented by the administrator, and conclude that the control capability of the administrator depends on not only one's degree but also the network average degree, and the AZDS implemented by the administrators with different degrees have different sensitivities to the change of economic incentive. Some real social networks with community structure illustrate the effectiveness of the AZDS by introducing the coarse graining method.

The location prediction issue impacts a wide range of practical areas ranging from urban planning to epidemic controlling. Numerous previous studies regarding location prediction have been carried out based on both individual historical trajectories and traces of the dense social ties of individuals such as friend and in-role. However, a kind of the so-called Familiar Stranger social tie has been discovered and identified recently, which was neglected previously and may improve the location prediction. In this paper, we propose a novel location prediction method which first takes the trajectories of the familiar stranger of individuals into account. We validate our method to achieve better performance with multiple social ties to predict locations of users with three empirical human traces datasets.

Weighted vertex cover (WVC), a generalized type of vertex cover, is one of the most important combinatorial optimization problems. In this paper, we provide a novel solution to the WVC problem from the view of network engineering. We model the WVC problem as an asymmetric game on weighted networks, where each vertex is treated as an intelligent rational agent rather than an inanimate one. Under the framework of asymmetric game, we find that strict Nash equilibriums of the asymmetric game are the intermediate states between the WVC states and the minimum WVC (MWVC) states. Besides, we propose best response algorithms with memory and feedback to solve the WVC problem, and find that a better approximate solution to the MWVC can be obtained under the feedback-based best response algorithm. Numerical illustrations verify the performance of the proposed game solution on weighted networks. Our findings pave a new way to solve the WVC problem from the perspective of asymmetric game, which opens a bottom-up avenue to address the combinatorial optimization problems.

Obstructive sleep apnea (OSA) is a popular sleep disorder. Traditional OSA diagnosis methods are cumbersome and expensive, which bring inconvenience for patient diagnosis and heavy workload for physician. Automatically identifying OSA patients from electrocardiogram (ECG) records is important for clinical diagnosis and treatment. In this paper, a new method based on the frequency and network domains is proposed to automatically recognize OSA patients with nocturnal ECG records. First, each RR-interval (beat to beat heart rate) series was divided into segments. By calculating the power spectral density (PSD) of heart rate variability segment with Lomb-Scargle method, the dynamic time warping (DTW) distance was used to evaluate the similarity (dissimilarity) of the lower frequency in the PSD series, then the DTW distance matrix was transformed to a binary matrix, and then network metrics were calculated to discriminate OSA patients with healthy subjects. The new method was tested with data of 389 subjects collected from two public databases that consist of normal subjects without OSA (apnea-hypopnea index, AHI 5) and OSA patients (AHI 5). Results show that a single network metric (local clustering coefficient) can recognize OSA patients with 90.1% accuracy, 88.29% sensitivity, and 90.5% specificity, and confirm the potential of using the ECG records for OSA patients recognition.

Understanding streaming user behavior is crucial to the design of large-scale Video-on-Demand (VoD) systems. In this article, we begin with the measurement of individual viewing behavior from two aspects: the temporal characteristics and user interest. We observe that active users spend more hours on each active day, and their daily request time distribution is more scattered than that of the less active users, while the inter-view time distribution differs negligibly between two groups. The common interest in popular videos and the latest uploaded videos is observed in both groups. We then investigate the predictability of video popularity as a collective user behavior through early views. In the light of the limitations of classical approaches, the Autoregressive-Moving-Average (ARMA) model is employed to forecast the popularity dynamics of individual videos at fine-grained time scales, thus achieving much higher prediction accuracy. When applied to video caching, the ARMA-assisted Least Frequently Used (LFU) algorithm can outperform the Least Recently Used (LRU) by 11--16%, the well-tuned LFU by 6--13%, and the LFU is only 2--4% inferior to the offline LFU in terms of hit ratio.

This paper designs a ground-target tracking system with unmanned aerial vehicles(UAVs), which consists of the states estimation of the ground target and the distributed model predictive control with two UAVs. In the tracking system, the extended kalman filter estimates the states of the target, and the distributed model predictive control in the UAVs is responsible to track the target and avoid collision. Both the fixed-wing and rotorcraft UAVs are successfully realized. Numerical simulations demonstrate the effectiveness of our tracking systems with the designed UAVs.

The vulnerability of a network may suffer tremendous economic losses. From the perspective of network nodes, network security depends not only on the behavior of a node but also on that of its neighbours. We propose a non-cooperative networking vaccination game with economic incentive mechanism under a susceptible-infected-susceptible (SIS) epidemic process. In the game each node individually decides on whether or not to invest in anti-virus protection strategies to get vaccinated such as install an anti-virus software or a firewall. On account of the competition of interests among nodes, we present a zero-determinant (ZD) strategy for a node as the administrator who takes the security of overall network into account and plays a major role in minimizing the social cost. Finally, we evaluate the performance of ZD strategies and compare the proposed method with other strategies.

Functional connectomes (FCs) are powerful in characterizing brain conditions. Temporal FC metrics can index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior. However, time-varying properties of temporal brain networks in general mental disorders have been less investigated. In this paper, FCs derived from resting-state fMRI (R-fMRI) data are temporally divided into quasi-stable segments by a sliding time window approach. Then we develop a new framework for accessing dynamic graph properties of temporal functional brain connectivity, and apply it to the healthy controls (HCs) and patients with chronic schizophrenia (SZs).

Human brain determines the ability of perceiving and behaving. Previous works have introduced network science and control theory to stimulate the brain states and functions. In this work, network controllability theory is utilized to study the structural controllability of both temporal and static functional brain networks. We find that only a few nodes are the required driver nodes to structurally control the static functional brain networks. Specially, some regions of interest (ROIs) in cerebellums with low connectivity play important roles in controlling functional brain networks. Structural controllability of temporal functional brain networks is more complicated. Control centrality of nodes which presents the ability to control other nodes does not only depend on the degree but also on the location of the nodes via studying the temporal functional brain network.

Despite the increasing popularity and the perceived promise of crowdsourcing, its openness presents individuals with an opportunity to exhibit antisocial behavior, such as free-ride and attack to decrease the social welfare, which is considered as a crowdsourcing contest dilemma. Hence, incentive mechanisms are needed to compel rational and selfish individuals to contribute well behavior in tasks. In this paper, we integrate the pricing and reputation schemes to design a novel socially optimal rating protocol based on game theory, in which each player is tagged with a rating to represent its social status, and players are encouraged to contribute good behaviors to increase their ratings, thus receive higher rewards. In particular, we analyze how the players' behaviors are influenced by the incurred costs and the designed payment, as well as their long-term utilities. By quantifying the sufficient and necessary conditions under which all players comply with the social norm in their self-interests, we formulate the rating protocol design problem, and analyze the impacts of the design parameters in order to characterize the optimal design, that maximizes the social welfare to achieve the social optimum. Finally, illustrative results show the validity and effectiveness of our proposed protocol design for crowdsourcing contest dilemma.

This paper extends our previous work on synchronizing a network of Kuramoto-oscillator digraph with a pacemaker. Compared with the minimal driven or pinned nodes of two linear coupling models, those nodes forced by the pacemaker are relative conservative in our Kuramoto model with nonlinear couplings. Comparative analyses of the results and dynamics are made among these three models. For two given directed networks, discussion are given to test whether less nodes forced by the pacemaker are feasible to achieve phase agreement or frequency synchronization in a Kuramoto-oscillator network.

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: 1) anatomizing the whole spread process among all subpopulations into disjoint componential patches; 2) inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; and 3) recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.

To uncover the process of spatial invasion of infectious diseases, it is vital to reconstruct the epidemic invasion pathways among subpopulations, which is the central interest of this work. The process inference is a challenging problem due to the complexity of spreading process on networked metapopulation. With the epidemic arrival time (EAT) infected series of each subpopulation, an invasion pathway inference algorithm is proposed based on probability theory and combinatorial mathematics. Simulation results on Barabási-Albert (BA) networked metapopulation are presented to verify the satisfactory performance of the proposed inference algorithm.

This paper addresses the asynchronous consensus problem of continuous-time second-order multi-agent systems with aperiodic sampled-data. Based on nonnegative matrix theory and graph theory, sufficient conditions guaranteeing the consensus of system are derived. It is proved that consensus can be reached if the communication digraph is strongly connected and the sampling intervals are restricted to a feasible region with respect to the control gain and the maximum in-degree of the communication digraph. A numerical example is provided to validate the theoretical result.

In self-organized mobile ad hoc networks (MANETs), network functions rely on cooperation of self-interested nodes, where a challenge is to enforce their mutual cooperation. In this paper, we study cooperative packet forwarding in a one-hop unreliable channel which results from loss of packets and noisy observation of transmissions. We propose an indirect reciprocity framework based on evolutionary game theory, and enforce cooperation of packet forwarding strategies in both structured and unstructured MANETs. Furthermore, we analyze the evolutionary dynamics of cooperative strategies and derive the threshold of benefit-to-cost ratio to guarantee the convergence of cooperation. The numerical simulations verify that the proposed evolutionary game theoretic solution enforces cooperation when the benefit-to-cost ratio of the altruistic exceeds the critical condition. In addition, the network throughput performance of our proposed strategy in structured MANETs is measured, which is in close agreement with that of the full cooperative strategy.

Modern information and communication technologies provide digital traces of human interactive activities, which offer novel avenues to map and analyze temporal features of human interaction networks. This paper explores mesoscopic patterns of human interactive activities from six real-world interaction networks with temporal-topological isomorphic subgraphs, i.e., temporal motifs. We discover two dominant mutual motifs, “Star,” “Ordered-chain,” and one dominant directed motif, “Ping-Pong,” which characterize the interactive patterns of “Leader,” “Queue,” and “Feedback,” respectively. Moreover,temporal dynamics shows that bursts are universal in human mesoscopic patterns, and the evolution of three dominant temporal motifs indicates the existence of characteristic time. Finally, we analyze temporal robustness and generalization to verify that 3-event temporal motifs are a simple yet powerful tool to capture the mesoscopic patterns of human interactive activities.

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales, which are ubiquitous in our economy, nature and society. Inspired by the description of a static network as a linear time-invariant (LTI) system in Lin and Liu's work, in this paper we consider a temporal network associated with a linear time-variant (LTV) system, and focus on structural controllability of temporal networks through a graphic tool with the time-ordered graph model. Both graphic interpretation and illustrative examples are given to understand structural controllability of temporal networks.

Community detection is a well-studied problem in network science. In this paper, a novel community detection algorithm based on evolutionary game dynamics is proposed, where individuals hold disperse opinions as their mixed strategies and play coordination games with their connected individuals in a network. Employing strategy updating processes among the population, the individuals finally fall into clusters of different opinionists which correspond to a community partition of the network. Using Zachary's karate club network as a benchmark test, the validity of the proposed community detection method is verified.

As a basic concept of complex network theory, clustering coefficient is vividly defined as the closeness of friend cliques in social networks. Now the availability of large-scale high-resolution empirical data allows researchers to study social networks with more details of time, and extend the static network topology with temporal dimension. Here we propose two definitions of temporal clustering coefficients, taking into account the consequence and the durations of link, respectively. With the verification of SIS (Susceptible-Infected-Susceptible) epidemic dynamics on three empirical temporal data sets, the Fudan Wi-Fi data set and two RFID human face-to-face data sets, we find that the epidemic threshold decreases with enhancing the temporal clustering of involved networks, while the one in the case of aggregated (static) networks can not present such dependence.

This paper reviews the recent advances in bridging time series analysis and complex network theory. We first introduce the main representative approaches of mapping time series into complex networks with their potential and advantages in applications in a variety of fields, and also report the latest methods to feature complex networks with typical time series after multi-scaling transformation, and to estimate and identify the uncertain connectivity of a complex network. As an example, we show an amplitude-temporal method to fast detect arrhythmia with the indicator of the average degree of associated networks.

This paper addresses the problem of consensus by using the model predictive control (MPC) with horizon one. We propose centralized and distributed MPC consensus protocols, and prove that both of them asymptotically solve the consensus problem. We also give numerical examples to show that the convergence speed can be significantly increased by simply adjusting the parameter in the MPC consensus protocols.

To solve the vertex cover problem in an agent-based and distributed networking systems utilizing local information, we treat each vertex as an intelligent rational agent rather than an inanimate one and provide a spatial-snowdrift-game-based optimization framework to vertex cover of networks. We analyze the inherent relation between the snowdrift game and the vertex cover: Strict Nash equilibriums of the spatial snowdrift game are the intermediate states between vertex-covered and minimal-vertex-covered states. Such equilibriums are obtained by employing the memory-based best response update rule. We also find that a better approximate solution in terms of the minimal vertex cover will be achieved by increasing the individuals' memory length, because such a process optimizes the individuals' strategies and helps them convert from bad equilibriums into better ones. Our findings pave a new way to solve the vertex cover problem from the perspective of agent-based self-organized optimization.

The information of both the position and velocity of agents are required in most existing flocking algorithms. This paper studies the model predictive control (MPC) flocking of a networked multi-agent system based on position measurements only. We first propose a centralized impulsive MPC flocking algorithm and further develop a feasible sequential-negotiation based distributed impulsive MPC flocking algorithm, where each agent sequentially solves a local optimization control problem involving the states of its neighbors only. We prove that both the centralized and distributed impulsive MPC flocking algorithms lead to a stable flock by using geometric properties of the optimal path followed by individual agents and provide numerical simulation examples to illustrate their effectiveness and advantages in convergence rate and communication cost.

This paper presents a frequency-degree mapping algorithm to transform time series into complex networks. The results induced from random and periodic time series demonstrate that the method can conserve both amplitude information and temporal information of the original time series. When applied to a practical problem of arrythmia detection, the method is able to distinguish ventricular fibrillation (VF) and monomorphic ventricular tachycardia (MVT) from normal sinus rhythm (NSR) with high accuracy.

Humans are suffering from the serious consequences of catastrophic accidents of complex systems that are initially triggered by only a small fraction of nodes' failures. We employ Olami-Feder-Christensen (OFC) model as a prototype of the avalanche dynamics to study cascading catastrophes on scale-free networks, and we propose a novel energy relaxation rule based on node betweenness, which suppresses the propagation failures from toppled nodes to the rest of the network and thus reduces the catastrophes' degree effectively.

It has been reported that collective properties of self organized agents may align with their density information, which are different from previous traditional models. In this paper, to design and analyze the collective behaviors, we present a gravity-based A/R(attraction/repulsion) model which is affected by the density information. With visualizing population density distribution of aggregation process, we find that a critical value of population density can trigger rapid transition of collective formation from disorder to highly synchronous states, which is consistent with some existing phenomena in nature. Meanwhile, motifs of collective formation patterns have been analyzed to show the connectivity patterns of multi-agent system at the micro-scale.

This paper studies the roles of small-world and degree heterogeneity features on the behavioral diversity in a structured population. The findings conclude that the collective behaviors are prone to the much higher diversity in a homogeneous structure due to the small-world effect, yet they are affected little in a scale-free structure. And the degree heterogeneity promotes the behavioral diversity a little while the population structure is homogeneous, but inhibits diversity while it is scale-free. Our work may be helpful for better understanding the collective evolutionary dynamic behaviors in a homogeneous or scale-free structured population.

In this paper, we propose a new selective pinning strategy, the BC-based pinning strategy, to control a complex network, i.e., placing the local feedback controllers on the vertices with high betweenness centrality (BC). To verify that the stabilizability bounds of a network depend on not only degrees of the pinned vertices, but also the distance between the pinned vertex set and the unpinned vertex set, we pin two real-world networks, the protein-protein network in yeast and the U.S.A. airline routing map, through the BC-based strategy, where the vertices' BC are weakly correlated with their degrees. Since the vertex's BC contain more information with the degree as well as the shortest path, our investigation shows that the former method yields better stabilizability than the latter one.

In this paper, we study cooperative behaviors on homogeneous networks with different topological randomness. We find that, compared with the square lattice, the topological randomness promotes the emergence of cooperation in the networked prisoner's dilemma game. In order to explain this phenomenon, we observe the dynamical coevolution and organization of cooperators and defectors, and find almost all the individuals are altering their strategies at the steady state. These fluctuating individuals are easier to compose larger clusters when the network becomes more disordering. Therefore, the cooperators in random homogeneous networks can protect themselves more efficiently from the exploitation of defectors than those in regular lattices.

Many real networked systems present the power-law degree distribution and display the scale-free feature. In this paper, we study the emergence of stable cooperators in the evolutionary game of scale-free networks with various degree exponents. Our investigations show that a heterogeneous network with small degree exponent promotes the emergence of stable cooperators, since the cooperative hubs with high degree can efficiently protect themselves from the invasion of defectors for high temptation. Moreover, the coexistence range of cooperators and defectors is widening as the network becomes heterogeneous, in which individuals are willing to hold onto pure cooperation or defection strategies.

Many real networks have the similar power-law form connection distribution. However, networks with the same degree distribution may have quite different dynamical behaviors and other topological properties. In this paper, a synchronization-preferential rewiring mechanism for enhancing the synchronizability of a dynamical network while keep the power-law degree distribution unchanged is proposed. We also find that, as the synchronizability of the network enhances, the characteristic path length and assortativity decrease, but the maximum betweenness centrality does not have obvious decreasing trend.

In the present work, we study the feedback control of a scale-free network of coupled Henon maps. A condition is derived for locally asymptotically stabilizing such a discrete-time complex dynamical network onto the homogenous stationary state by applying local feedback controllers to a fraction of network nodes. Numerical studies verify the effectiveness.

The increasing outbreaks of computer viruses lead to a significant threat to the Internet. In this review we firstly give a brief introduction of computer virus including definition, classification, and characteristics. Then we turn on the overview that how viruses spread on networks in different topologies, especially in the context of computer viruses spreading on the Internet. Recent epidemic studies on the uncorrelated and correlated networks in terms of homogeneity and heterogeneity have been reviewed and compared their distinctions with regard to the main characteristic in epidemiology: epidemic threshold.

A linear model of recurrent neural networks, called the Elman networks, is combined with the simple nonlinear modulo (mod) operation on its linear activated function so as to generate chaos purposely. Conditions on the weight matrix are obtained, under which the generated chaos satisfies the mathematical definition of chaos in the sense of T.Y. Li and J.A. Yorke (1975). Some simple and representative weight matrices are constructed for designing such Elman networks that can generate Li-Yorke chaos. Several numerical simulations are shown to verify and visualize the design.