Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to uncover the internal mechanisms of the emergence and evolution of communities in networks. To overcome the above limitation, in consideration of the fact that a network can be regarded as a pattern composed of communities, we introduce Turing pattern dynamic as theory support to construct the network evolution model. Specifically, we develop a Reaction-Diffusion model according to Q-Learning technology (RDQL), in which each node regarded as an intelligent agent makes a behavior choice to update its relationships, based on the utility and behavioral strategy at every time step. Extensive experiments indicate that our model not only reveals how communities form and evolve, but also can generate networks with the properties of scale-free, small-world and assortativity. The effectiveness of the RDQL model has also been verified by its application in real networks. Furthermore, the depth analysis of the RDQL model provides a conclusion that the proportion of exploration and exploitation behaviors of nodes is the only factor affecting the formation of communities. The proposed RDQL model has potential to be the basic theoretical tool for studying network stability and dynamics.

The classification of heartbeats is an important method for cardiac arrhythmia analysis. This study proposes a novel heartbeat classification method using hybrid time-frequency analysis and transfer learning based on ResNet-101. The proposed method has the following major advantages over the afore-mentioned methods: it avoids the need for manual features extraction in the traditional machine learning method, and it utilizes 2-D time-frequency diagrams which provide not only frequency and energy information but also preserve the morphological characteristic within the ECG recordings, and it owns enough deep to make better use of performance of CNN. The method deploys a hybrid time-frequency analysis of the Hilbert transform (HT) and the Wigner-Ville distribution (WVD) to transform 1-D ECG recordings into 2-D time-frequency diagrams which were then fed into a transfer learning classifier based on ResNet-101 for two classification tasks (i.e., 5 heartbeat categories assigned by the ANSI/AAMI standard (i.e., N, V, S, Q and F) and 14 original beat kinds of the MIT/BIH arrhythmia database). For 5 heartbeat categories classification, the results show the F1-score of N, V, S, Q and F categories areF$_{N}$0.9899,F$_{V}$0.9845,F$_{S}$0.9376,F$_{Q}$0.9968,F$_{F}$0.8889, respectively, and the overall F1-score is 0.9595 using the combination data balancing. The results show the average values for accuracy, sensitivity, specificity, predictive value and F1-score on test set for 14 beat kinds the MIT-BIH arrhythmia database are 99.75%, 91.36%, 99.85%, 90.81% and 0.9016, respectively. Compared with other methods, the proposed method can yield more accurate results.

In offline or online worlds, many social systems can be represented as signed social networks including both positive and negative relationships. Although a variety of studies on signed social networks have been conducted motivated by the great application value of unique polarity characteristics, how to model the process of influence propagation over signed social networks is still an important problem that remains pretty much open. Currently, a few studies extended traditional diffusion models (e.g., Independent Cascade model and Linear Threshold model) from unsigned social networks to signed social networks for estimating positive and negative influence of user sets. However, all of above extension models are stochastic and descriptive models. In order to ensure the accuracy of estimated influence, existing models require a significant number of Monte-Carlo simulations which are very time-consuming and not scalable. Aiming at this issue, we propose the Polarity-related Linear Influence Diffusion (PLID) model which can quickly and accurately calculate polarity-related influence of user sets without simulations. To validate effectiveness and efficiency of our proposed model, we make use of our PLID model to solve the positive influence maximization problem in signed social networks under rigorous mathematical proofs. Extensive experiments demonstrate that our PLID model and approximation algorithm significantly outperform state-of-the-art methods in terms of positive influence spread and running time, using Epinions and Slashdot datasets.