General Complex Networks Research Articles

A hypernetwork-based urn model for explaining collective dynamics.

The topological characterization of complex systems has significantly contributed to our understanding of the principles of collective dynamics. However, the representation of general complex networks is not enough for explaining certain problems, such as collective actions. Considering the effectiveness of hypernetworks on modeling real-world complex networks, in this paper, we proposed a hypernetwork-based Pólya urn model that considers the effect of group identity. The mathematical deduction and simulation experiments show that social influence provides a strong imitation environment for individuals, which can prevent the dynamics from being self-correcting. Additionally, the unpredictability of the social system increases with growing social influence, and the effect of group identity can moderate market inequality caused by individual preference and social influence. The present work provides a modeling basis for a better understanding of the logic of collective dynamics.

Eigenvector-based analysis of cluster synchronization in general complex networks of coupled chaotic oscillators

Eigenvector-based analysis of cluster synchronization in general complex networks of coupled chaotic oscillators

Tetrel‐Centered Exchange Cascades to Decouple Inhibition and Induction of Thiol‐Mediated Uptake: Introducing Cell‐Penetrating Thiolactones, Focus on Reversible Michael Acceptor Dimers

AbstractThis study adds thiolactones to the collection of cascade exchangers (CAXs) and reports with dynamic covalent Michael acceptor dimers the first CAXs that rather inhibit than enable thiol‐mediated uptake. The superior performance of dimers compared to monomeric Michael acceptors as inhibitors might suggest that top inhibitors move along thiol/ate arrays without losing covalent contact, while cell penetrators exchange along disulfide arrays. This decoupling of inhibition and induction of thiol‐mediated uptake is important because the latter is promising for drug delivery, while the former possibly helps to reveal targets associated with antiviral, antithrombotic and antitumor activity, leading to drug discovery. These results are consistent with the working model of thiol‐mediated uptake as a general complex network in charge of bringing matter into cells, and thus support its validity.

Finite-time pinning impulsive synchronization of stochastic complex networks with mixed delays

This paper mainly considers the finite-time synchronization (FTS) of stochastic complex networks with mixed delays via pinning impulsive control. Firstly, a novel pinning impulsive controller is introduced by selecting the nodes of the stochastic complex network with large error norms, which not only saves control resources, but also ensures that stochastic complex network with mixed delays can realize FTS. It is worth noting that the finite-time control strategy constructed is not affected by the sign function. Secondly, a uniform FTS criterion under synchronizing impulses and desynchronizing impulses is derived based on the idea of average impulsive interval. Moreover, the obtained sufficient condition are further extended to general stochastic complex networks and stochastic complex networks with mixed delays under delayed impulses. Finally, the feasibility of the theory results is verified by a numerical example.

Mean-square synchronization of fractional-order stochastic complex network via pinning control

In this paper, the mean-square synchronization problem is considered for the fractional-order stochastic complex network (FOSCN) via pinning control. Firstly, suppose that the dynamics of the node are modeled by continuous-time nonlinear fractional stochastic differential equations (FSDEs). Next, the pinning nodes are selected according to the low-dimensional pinning criteria. Furthermore, the controller is designed for each pinning node, and a set of sufficient conditions are given to guarantee the mean-square synchronization. Then, the convergence analysis of the closed-loop system is finished by directly utilizing the properties of the integral solution. It proved that the mean-square synchronization of FOSCN can be achieved with the designed controller under some conditions. In addition, the synchronization problem of general fractional-order complex networks (FOCN) without stochastic noise is solved by using a similar method. Finally, compared simulation examples are performed to demonstrate the effectiveness of the proposed methods.

Mean-square pinning control of fractional stochastic discrete-time complex networks

This paper considers the mean-square pinning control problem of fractional stochastic discrete-time complex networks. First, a new fractional stochastic discrete-time complex networks model with stochastic noise is established. Then, some pinning controllers and sufficient conditions are developed for the complex networks. By adopting Lyapunov energy function theory and matrix analysis theory, it proved that the synchronization of the fractional stochastic discrete-time complex networks can be achieved in a mean-square sense via pinning control. In addition, these results are extended to solve the synchronization problem of general fractional discrete-time complex networks without noise. Finally, several numerical examples are given to verify the correctness of the obtained theoretical results.

Statistical Identification of Important Nodes in Biological Systems.

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

Statistical Identification of Important Nodes in Biological Systems.

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

Link Prediction through Deep Generative Model.

SummaryInferring missing links based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine, e-commerce, social media, and criminal intelligence. Numerous methods have been proposed to solve the link prediction problem. Yet, many of these methods are designed for undirected networks only and based on domain-specific heuristics. Here we developed a new link prediction method based on deep generative models, which does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities. When applied to various real-world networks from different domains, our method shows overall superior performance against existing methods.

Dynamical Network Analysis of the South Korean Dialects Compared to Traditional Dialect Classification

In this paper, we investigate the network properties of the South Korean dialects. Among 153 words in “The Linguistic Atlas of Korea”, 33 words satisfying our criteria were surveyed. We set 138 basic administrative districts that make up South Korea as nodes and construct a language network by using a defined procedure. The network shows neither a small-world (small-world coefficient < 5) nor a scale-free (degree distribution forms like a unimodal Poisson) property in the general complex network. Also, the assortativity is seen to show transition from negative to positive at about the threshold value of 15. The number of Korean linguistic groups, which is the most import result in this study, is constantly four with s-curved Newman modularity in the threshold range 13 ∼ 22. The Korean dialect division by using a network modularity method is almost identical to the classification of traditional linguists (Central, Southwest, Southeast, and Jeju). Furthermore, subgroups can be obtained by performing a hierarchical dialect division. As a result, South Korean dialects are classified into seven minor groups, and some regions (e.g., Southern Gang-won) belong to groups different from those of the traditional classification method.

Neural sliding-mode pinning control for output synchronization for uncertain general complex networks

A novel approach for output synchronization for uncertain general complex networks with non-identical nodes is proposed. This goal can be solved applying to a small fraction of network nodes (pinned ones) a neural controller; this controller is composed of an on-line identifier based on a recurrent high-order neural network, and a sliding-mode controller. An illustrative example is included, which is composed of a network of ten nodes, with different self-dynamics, illustrating the effectiveness and good performance of the proposed control scheme.

Periodically intermittent discrete observation control for synchronization of the general stochastic complex network

In this paper, the exponential synchronization of stochastic complex networks without strong connectivity is discussed. Distinguished from the previous papers, we introduce a novel periodically intermittent control based on discrete-time state observations, instead of continuous-time state observations in the control time. The coupling structure of networks is time-varying. By employing the Kirchhoff’s Matrix Tree Theorem in graph theory and Tarjan’s algorithm, some sufficient conditions are derived. In particular, when the novel control degenerates into the feedback control based on discrete-time state observations, it is discussed in detail as well. Furthermore, we apply our main results to study the synchronization of stochastic coupled oscillators with time-varying coupling structure. Meanwhile, two synchronization criteria are derived. Finally, a numerical test is given to validate the effectiveness of our results.

Global synchronization under PI/PD controllers in general complex networks with time-delay

In complex networks, global synchronization problems have been widely studied over the past ten years due to its extensive applications. One of the challenge issue is how to design an available controller to make the complex networks reach global synchronization, especially with directed topologies and time-delay. As we all know, the classical proportional-integral-derivative (PID) controller is widely and successfully used in engineering systems with nonlinear dynamics, however, global synchronization under PI/PD controllers in general directed complex networks with time-delay is still unsolved. This paper proposes several sufficient conditions of global synchronization for directed networks with time-delay by designing appropriate PI/PD controllers, and gives some simple numerical examples to validate the main results.

Generating Scale-Free Topology for Wireless Neighborhood Area Networks in Smart Grid

Neighborhood area networks (NANs) are critical infrastructure in smart grid to support communications. With the development of wireless communication technologies, there is a great potential for large-scale wireless-mesh NANs to be deployed to smart grid systems. While there exist theoretical models of complex networks, they are not directly applicable in the generation of network topology for wireless-mesh NANs due to their lack of some specifications unique in smart grid, leading to difficulties in theoretical studies of smart grid NANs. Among various topological structures, scale-free networks are considered as a potential resolution. This paper proposes a systematic approach to generate scale-free topology of wireless-mesh NANs in smart grid. A theoretical analysis confirms the scale-free feature of the network topology generated from the approach. To reveal the characteristics of our approach in comparison with widely used models of general complex networks, simulations are conducted on various network parameters including network diameter, average path length, clustering coefficient, and fractal dimension. The simulation results show that our approach for generating wireless-mesh NAN topology is fundamentally different from these compared models, and thus is a new type of complex-network model. Specifically, our model is suitable for wireless-mesh NANs in smart grid.

Analytical probability propagation method for reliability analysis of general complex networks

Reliability analysis of complex networks is often limited by large and exponentially increasing computational requirements with system size. In this paper, a new approximated analytical method the authors call the probability propagation method (PrPm) is proposed to calculate the reliability of general complex networks. The proposed method originates from the idea of belief propagation for inference in network graphs to pass a joint probability distribution between nodes in the network. At each step, the distribution is updated and passed as the message to its direct neighbors. After the message passes to the terminal node, an approximation of the network reliability is found. In this paper, the derived updating rules for message passing are provided, as well as a precise formulation of the error compared to the exact solution. The method is applied to three test applications: an example from a previous study on network reliability, a power distribution network, and a general grid network. Results from these applications show high accuracy for the proposed method compared to exact solutions where possible for comparison. In addition, the authors show orders of magnitude increases in computational efficiency of PrPm compared to existing approaches. This includes reducing the computational cost for analyses from an exponential increase in computation time with the size of the system to a quartic increase. The proposed PrPm enables accurate and computationally tractable reliability assessments of larger, complex networks.

Cluster synchronization of Markovian switching complex networks with hybrid couplings and stochastic perturbations

In this work, we extend cluster synchronization into a more general complex network model, a class of Markovian switching complex networks composed of nonidentical nodes with hybrid couplings. These networks are also influenced by coupling delays and disturbed by stochastic noise perturbations. Based on some definitions, assumptions and lemmas, an adaptive controller is designed. Particularly, by constructing a novel stochastic Lyapunov–Krasovskii function and employing the linear matrix inequalities (LMIs), Ito’s formula and the stochastic Lasalle-type invariance theorem, the effectiveness of the designed controller is proved. Besides, a numerical example is illustrated for further verifications and conclusions.

Node Importance in Controlled Complex Networks

The problem of node importance in a general controlled complex network is investigated in this brief. An index, ControlRank index, to measure the node importance is presented. The index is relatively intuitive and helps unify various indexes that are effective only in the networks with some particular topology. If all nodes except one are pinned in a network, the importance of the unpinned node is sorted in descending order of degree; even if a node is the most significant node, it does not necessarily belong to the most efficient node set; it is very crucial to have an eye on the information balance as for information dissemination in a network; it is reasonable to rank node importance by sorting the node degrees in a scale-free network. The results and phenomena provide us with a broader and clearer perspective to view node importance in complex networks.

Fixed-Time and Finite-Time Synchronization for a Class of Output-Coupling Complex Networks via Continuous Control

This paper mainly investigates the finite-time and fixed-time synchronization problem for a class of general output-coupling complex networks with output feedback nodes. The fixed-time and finite-time synchronization protocols are presented based on continuous controller strategies which can efficaciously eliminate chattering phenomenon existing in some previous results. Several sufficient conditions ensuring fixed-time and finite-time synchronization are derived by employing Lyapunov stability theory, linear matrix inequality (LMI) and adaptive technique. Furthermore, aimed at the model of this article, we study the problem of adaptive coupling strength in fixed-time synchronization which is rarely involved in previous results. Finally, several numerical examples are given to illustrate the effectiveness of our results.

Synchronization of IT2 stochastic fuzzy complex dynamical networks with time-varying delay via fuzzy pinning control

In this paper, the problem of synchronization on interval type-2 (IT2) stochastic fuzzy complex dynamical networks (CDNs) with time-varying delay via fuzzy pinning control is fully studied. Firstly, a more general complex network model is considered, which involves the time-varying delay, IT2 fuzzy and stochastic effects. More specifically, IT2 fuzzy model, as a meaningful fuzzy scheme, is investigated for the first time in CDNs. Then, with the aid of Lyapunov stability theory and stochastic analysis technique, some new sufficient criteria are established to ensure synchronization of the addressed systems. Moreover, on basis of the parallel-distributed compensation (PDC) scheme, two effective fuzzy pinning control protocols are proposed to achieve the synchronization. Finally, a numerical example is performed to illustrate the effectiveness and superiority of the derived theoretical results.