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In this paper, we propose a speed-accelerating method (SAM) for synchronization control of networked mobile chaotic agents. This method is based on the framework of connection adaption strategy (CAS) which is considered to be a simpler scheme than commonly used coupling adaption strategies. Our proposed method is shown to be able to lift the constrain in the CAS and the performance of the control is systematically enhanced. The mechanism of the SAM has been carefully analyzed. It is found that the agents’ motion speed could be classified into two regimes. The underlying mechanisms of the SAM that drive the network into synchronization in the two regimes are shown to be different with detailed elaborations.

The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

Link prediction is a network analysis method to solve practical problems and has important research value in many fields. In the field of traditional Chinese medicine, according to the different needs of the disease and the different characteristics of the drugs, the combination of the two drugs is called drug pairing. Based on traditional Chinese medicine network, this study provides a new research perspective based on complex network and link prediction. Aiming at the problem of strong randomness in the traditional random walk, a new link prediction method based on graph embedding method is proposed in this paper. Compared with the traditional random walk, the index proposed in this paper improves the performance greatly. Through link prediction, it is verified that the drug pair without compatibility in the existing prescription may appear in the future prescription, as a supplement to the new drug pair.

Multiplex networks, where nodes may interact through several different types of links, have been used widely to describe various complex systems. Many complex systems have an important feature that pairs of nodes may be connected by different types of links at the same time. In order to study the effect of the overlapped links systematically, in this paper I propose an algorithm to generate general multiplex networks with overlapped links. This algorithm could construct multiplex networks with arbitrary degree–degree distribution in each layer, and more importantly, could further adjust the proportion of overlapped links over a wide range. Therefore, the algorithm proposed in this paper offers an approach to construct multiplex networks into a delicate extent, which may provide a formal setting to study the topological impact of multiplex networks in a systematic manner.

The index of link prediction based on random walk usually has the same transition probability in the process of particle transfer to its neighbor nodes, which has strong randomness and ignores the influence of the particularity of network topology on particle transition probability. In order to resolve this problem, this paper proposes a random walk with restart index based on graph embedding (GERWR). The algorithm uses graph embedding method to randomly sample network nodes and generate node representation vectors containing potential network structure information. By calculating the similarity of node vectors, it redefines a biased transition probability. We apply it to the process of random walk and explore the influence of the particularity of network topology on the transition during the particles walk. Finally, based on biased transition, the index proposed in this paper is compared with five classical similarity indexes in unweighted networks. The results show that the prediction algorithm based on graph embedding method with biased transfer has higher accuracy than other indexes.

A typical traditional Chinese medicine (TCM) prescription (or formula) is composed of one or more kinds of herbs, which can play a certain role according to the appropriate dosage and production method. The number of possible TCM prescription is nearly as large as that of chemical structures, so the development of quantitative prescription-efficacy relationship models is as appealing as to build a quantitative structure-efficacy relationship model. In this paper, we first construct a binary network of prescriptions and herbs based on the real TCM data. Through the analysis of the network, we propose a method to calculate the expected value of herbs based on the node similarity, which represents the probability that two kinds of herbs exist in the same prescription. Moreover, we use the hierarchical clusters to classify the herb pairs whose expected value is greater than a certain value, and observe which herbs belong to the same category. In addition, we predict the efficacy of herbs by both classic K-means algorithm and improved K-means algorithm which is based on the node similarity. After verification, the results show that the improved algorithm can predict the efficacy of traditional Chinese medicine more accurately comparing with the classic K-means algorithm. Therefore the model can be used for predicting the potential efficacy of traditional Chinese medicine.

Multiplex networks have been used to describe multilevel system by the way of combining several layers of sub-networks with one layer representing one sub-level system. Many multiplexes are characterized by a significant shared links in different layers. For this new network framework, research efforts have been paid on the study of its topological and dynamical properties. However, in these studies the network structures are mostly simple or specific, and the shared links in different layers has been mostly neglected, despite the fact that it is an ubiquitous phenomenon in most multiplexes. To systemically study multiplex network, a general multiplex network framework is necessary. In this work, we introduce an algorithm to generate a multiplex networks with shared links whose degree correlation functions of all its layers, the nodal degree correlation function between the layers, and the size of the resulting network are all tunable. Moreover, we give an upper bound of the average degree of the resulting network when the degree correlation functions introduced above are given and make efforts to maximize the average nodal degree of the multiplex networks. This algorithm may serve as a good candidate of standard technique for multiplex network generation.

The rapid development of modern society continually transforms the social structure which leads to an increasingly distinct dual structure of higher population density in urban areas and lower density in rural areas. Such structure may induce distinctive spreading behavior of epidemics which does not happen in a single type structure. In this paper, we study the epidemic spreading of mobile agents on dual structure networks based on SIRS model. First, beyond the well known epidemic threshold for generic epidemic model that when the infection rate is below the threshold a pertinent infectious disease will die out, we find the other epidemic threshold which appears when the infection rate of a disease is relatively high. This feature of two thresholds for the SIRS model may lead to the elimination of infectious disease when social network has either high population density or low population density. Interestingly, however, we find that when a high density area is connected to a low density may cause persistent spreading of the infectious disease, even though the same disease will die out when it spreads in each single area. This phenomenon indicates the critical role of the connection between the two areas which could radically change the behavior of spreading dynamics. Our findings, therefore, provide new understanding of epidemiology pertinent to the characteristic modern social structure and have potential to develop controlling strategies accordingly.

Epidemic spreading processes on top of multiplex networks have more dynamical properties than on single layered networks. To describe the intertwined processes on such networks, heterogeneous mean field approach (HMF) have been proposed for the continuous-time processes. However, it has been shown that the time evolution of infected individuals and the final epidemic size obtained from this approach have remarkable discrepancy with those from the Monte Carlo simulations. In this paper, we extend effective degree theory (EDT) for analyzing continuous-time epidemic processes on multiplex networks based on SIR epidemic model. Predictions obtained from the theory are shown to have excellent agreement with Monte Carlo simulations.

Link prediction is an important problem in topics of complex networks, which can be applied to many practical scenarios such as information retrieval and marketing analysis. Strategies based on random walk are commonly used to address this problem. In common practice of a random walk, a link predictor may move from one node to one of its neighbors with uniform transferring probability regardless of the characteristics of the local structure around that node, which, however, may contain useful information for a successful prediction. In this paper, we propose a refined random walk approach which incorporates graph embedding method. This approach may provide biased transferring probabilities to perform random walk so as to further exploit topological properties embedded in the network structure. The performance of proposed method is examined by comparing with other commonly used indexes. Results show that our method outperforms all these indexes reflected by better prediction accuracy.

The connection adaption strategy (CAS) has been proposed for the synchronization of networked mobile chaotic agents, which is considered to be a simpler scheme compared to commonly used coupling adaption strategies. However, this strategy only provides a limited range of feasible coupling strength allowing a success control. In this paper, we develop the CAS by introducing a jump process to resolve this problem. We show that the proposed approach systematically outperforms the original CAS in the whole range of the mobility and the range of feasible coupling strength is extensively expanded. In addition, we show that motion of the agents could be classified into three different regimes. The dynamical features of these motion regimes are analyzed and relevant measures are provided to characterize the controllability of the network in each regime. Ministry of Education (MOE) Published version This work was supported in part by the National Natural Science Foundation of China under Grant 11835003, in part by the Ministry of Education, Singapore, under Contract MOE2016-T2-1-119, and in part by NSF Grant PHY-1505000 and by DTRA Grant HDTRA1-14-1-0017.

Retargeting aims at adapting an original high-resolution photograph/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Typically, only low-level visual features are exploited, and human visual perception is not well encoded. In this paper, we propose a novel retargeting framework that rapidly shrinks a photograph/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserving graph ranking algorithm, which efficiently selects a few salient object patches to mimic the human gaze shifting path (GSP) when viewing a scene. Afterward, an aggregation-based CNN is developed to hierarchically learn the deep representation for each GSP. Based on this, a probabilistic model is developed to learn the priors of the training photographs that are marked as aesthetically pleasing by professional photographers. We utilize the learned priors to efficiently shrink the corresponding GSP of a retargeted photograph/video to maximize its similarity to those from the training photographs. Extensive experiments have demonstrated that: 1) our method requires less than 35 ms to retarget a $1024\times 768$ photograph (or a $1280\times 720$ video frame) on popular iOS/Android devices, which is orders of magnitude faster than the conventional retargeting algorithms; 2) the retargeted photographs/videos produced by our method significantly outperform those of its competitors based on a paired-comparison-based user study; and 3) the learned GSPs are highly indicative of human visual attention according to the human eye tracking experiments.

Epidemic is a hot issue in the public safety field. In this paper, we construct a two-layered networks to analyze that how the information diffusion prevents an epidemic spreading in the population. We used the effective degree theory to analyze the the spreading process which shows the results in high accuracy. Compared with the result of the mean field theory, we find our results obtained from the effective degree theory is highly accord to the numerical simulations, where regular networks, random networks, and scale free networks are examined. We therefore conclude that the effective degree theory could be successfully extended to two-layered networks with high accuracy and reliability.

A disease propagating in a community or a rumor spreading in a social network can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. Suppose that a disease or a rumor originating from a single source among a set of suspects spreads in a network, how to locate this disease/rumor source? This problem is crucial and challenging in different fields of computer or social networks, which is made more difficult in many applications where we have access only to a limited set of observations. We study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible-infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.

There is an ever-increasing interest in studying temporal networks nowadays, as temporal networks can illustrate the real-world system more accurately. To date, how to characterize nodes' importance is still unclear in temporal networks. In this work, we first use a time window graph model to cut the temporal network into slices, and then we give an indicator for network centrality according to the edge-based κ-shell decomposition for the temporal networks, which is named as temporal κ-shell decomposition. We mainly use the size of the largest component after removing the nodes with large centrality value to test the method's performance. The numerical experiments on several real networks indicate that the temporal κ-shell method outperforms some other indicators, and the results with different time window size show that the improvement is also robust.

A rumor spreading in a social network or a disease propagating in a community can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. Suppose that a rumor or a disease originating from a single source among a set of suspects spreads in a network, how to root out this rumor/disease source? This problem is important and challenging in different contexts of computer or social networks, which is made more difficult in many applications where we have access only to a limited set of observations. We study the problem of estimating the origin of a rumor/epidemic outbreak: given a contact network and a snapshot of epidemic spread at a certain time, determine the infection source. Assuming that the epidemic spread follows the usual susceptible-infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present a strategy and show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the rumor/epidemic spreading process not only in the local particular regime but also for the whole general regime.

A disease propagating in a community or a rumor spreading in a social network can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. Suppose that a disease or a rumor originating from a single source among a set of suspects spreads in a network, how to locate this disease/rumor source based on a limited set of observations? We study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible-infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.

Considering the fact that the contact strengths among people are diverse both in the duration time and the distance, in this paper, we study the epidemic dynamics with susceptible–infective–susceptible (SIS) model on a weighted adaptive network to emphasize this contact feature. In this model, the weight of a link denotes the contact strength between two individuals connected by this link, and each susceptible individual may adaptively transfer the weight from a link to another. We find that this weight adaption process could significantly aggravate the prevalence of an epidemic. Moreover, we examine the effectiveness of the link-removal strategy with our model, and the results show that the weight adaption process may weaken the efficiency of the strategy. The theoretical analysis is supported by the simulation results.

Identifying sources of epidemic spreading or rumor diffusion from minority data is of paramount importance in network science with great applied values to the society. However, a general theoretical frame work dealing with source(s) localization is lacking of perfect understanding. Based on limited observers in the network, we study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible–infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.

We introduce a model of a cyclic game. Designed to take advantage of the recurring nature of certain economic and social situations, a cyclic game differs from an extensive form game in that a cyclic game does not necessarily have an end. The same situations, although with different players, may be repeated infinitely often. In this paper, the domain evolution process in a novel kind of cyclic game in regular networks is investigated by simulation method. Different with the classical cyclic games, this process is called “creation-annihilation process”, which likes the autocatalysis reactions. We consider the regular networks with the situations of full occupied, partial occupied and gradient occupied. The results of numerical simulations show that the evolutions of such “creation-annihilation” cyclic games in regular networks with three, four or five states have some special feature: the evolution of three states can spread throughout the whole networks; however, the cases of four or five states usually has a stable boundary, and the growth is driven by the internal-evolution of the domain. Considering with the widespread of the cyclic autocatalysis in organism activities, such internal-evolution driven growth could be universal in many organism systems.

Multiplex networks have been used to describe multilevel system by the way of combining several layers of sub-networks with one layer representing one sub-level system. For this new network framework, research efforts have been paid on the study of its topological and dynamical properties. However, in these studies the network structures are mostly simple or specific. To systemically study multiplex network, a general multiplex network framework is necessary. In this work, we introduce an algorithm by which we could generate a multiplex network whose degree correlation functions of all its layers, the nodal degree correlation function between the layers, and the size of the resulting network are all tunable. Moreover, we give an upper bound of the average degree of the resulting network when the degree correlation functions introduced above are given. This algorithm may serve as a good candidate of standard technique for multiplex network generation.

There is growing interest in multiplex networks where individual nodes take part in several layers of networks simultaneously. From transportation networks to complex infrastructures, and to social and communication networks, a large variety of systems can be described in terms of multiplexes formed by a set of nodes interacting through different layers of networks, for example, in social networks where each individual node has different kinds of social ties or transportation systems where each location is connected to another location by different types of transport. Many of these multiplexes are characterized by a significant overlap of the links in different layers. However, so far the overlap of the links in different layers has been mostly neglected, despite the fact that it is an ubiquitous phenomenon in most multiplexes. In this paper we introduce a general algorithm to generate a multiplex networks with overlapped links. In this approach, we make efforts to maximize the average nodal degree of the multiplex networks. We hope the algorithm will shed light on the multiplex networks generations.

We introduce a weighted adaptive network model to investigate the epidemic dynamics based on a susceptible-infective-susceptible (SIS) pattern, where the weight of a link describes the contact strength between two connected individuals. In the model, a susceptible node is able to transfer the weight of the link connecting an infected neighbor to a link connecting one of its susceptible neighbors. It is found that this weight adaption process could strongly aggravate the destructiveness of an epidemic and leads to a new population relation. Moreover, the effectiveness of a simple epidemic control strategy on a weighted adaptive network is examined. The results show that the weight adaption process may reduce the strategy efficiency. Analysis are presented and the results support our numerical simulations.

Signal detection is generally related to network structure in both biological and engineering systems, and enormous effort has been putting into understanding the mechanism of amplification of weak signals in the aspects of self-tuning and scale-free topology. Here, we show that a third way of signal amplification exists, which does not require the scale-free topology as a necessary condition. This approach can effectively amplify the signal at an arbitrary node in a complex network by adaptively adjusting the coupling weights between the signal node and its neighbors, and thus can be used in both the local and global areas of a complex network. A theory is provided to explain its mechanism.

We study the influence of randomly distributed phase directions of external force in an array of coupled pendula, instead of studying the influence of continuous phase. We find that with the increase of the absolute value of the phase, the chaotic behaviors of the coupled arrays may be controlled and different synchronized patterns can be induced. These results demonstrate that by introducing the randomness of the phase directions, rather than the continuous value of the phase, it can lead to a synchronization in nonlinear systems. This finding may provide a new insight for understanding the mechanism of disorder induced synchronization.

It is known that for chaotic flows, a weak coupling does not always make the coupled systems approach synchronization but sometimes makes them become more complicated (Phys. Rev. E, 67 (2003) 045203(R)). We here report that a similar situation also occurs in the coupled chaotic maps, where a weak coupling will make the number of direction-phase clusters Nc increase. We find a double-resonance effect on the coupling strength e, where the first resonance comes from the coupling-induced periodic behaviors and the second one is due to the disappearance of the disorder phase. The mechanism of the second resonance is revealed through the out-of-phase links. Moreover, we show that the critical coupling ec of the maximum Nc will increase rapidly with the bifurcation parameter μ but slowly with the range of the distribution of non-identical oscillators.

The abnormal phase order of coupled logistic maps, i.e., the ratio of two sequential "up phases" in the total iterations, can be characterized by the direction phase (Phys. Rev. Lett., 84 (2000) 2610). We here consider the case of coupled logistic maps on complex networks and study how the network topology influences the abnormal phase order. Our numerical simulations reveal that the critical point for the appearance of abnormal phase order increases with the coupling strength but decreases with the degree of heterogeneity of complex networks. Moreover, we find that unlike in the case of normal phase order, it is possible for the system to show a periodic window in the case of abnormal phase order, but only within an appropriate range of coupling strengths, and finally, that the heterogeneity can reduce the maximum number of the phase clusters in a given periodic window.