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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.

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.

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.

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.

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 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.

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.