The Naming Game has proven to be an important model of opinion dynamics in complex networks. It is significantly enriched by the introduction of nodes committed to a single opinion. The resulting model is still simple but captures core concepts of opinion dynamics in networks. This model limitation is rigid commitment which never changes. Here we study the effect that making commitment variable has on the dynamics of the system. Committed nodes are assigned a commitment strength, w, defining their willingness to lose (in waning), gain (for increasing) or both (in variable) commitment to an opinion. Such model has committed nodes that can stick to a single opinion for some time without losing their flexibility to change it in the long run. The traditional Naming Game corresponds to setting w at infinity. A change in commitment strength impacts the critical fraction of population necessary for a minority consensus. Increasing w lowers critical fraction for waning commitment but increases this fraction for increasing commitment. Further, we show that if different nodes have different values of w, higher standard deviation of w increases the critical fraction for waning commitment and decrease this fraction for increasing commitment.

Popularity prediction is a key problem in networks to analyze the information diffusion, especially in social media communities. Recently, there have been some custom-build prediction models in Digg and YouTube. However, these models are hardly transplant to an incomplete social network site (e.g., Flickr) by their unique parameters. In addition, because of the large scale of the network in Flickr, it is difficult to get all of the photos and the whole network. Thus, we are seeking for a method which can be used in such incomplete network. Inspired by a collaborative filtering method-Networkbased Inference (NBI), we devise a weighted bipartite graph with undetected users and items to represent the resource allocation process in an incomplete network. Instead of image analysis, we propose a modified interdisciplinary models, called Incomplete Network-based Inference (INI). Using the data from 30 months in Flickr, we show the proposed INI is able to increase prediction accuracy by over 58.1%, compared with traditional NBI. We apply our proposed INI approach to personalized advertising application and show that it is more attractive than traditional Flickr advertising.

With a steadily growing population and rapid advancements in technology, the global economy is increasing in size and complexity. This growth exacerbates global vulnerabilities and may lead to unforeseen consequences such as global pandemics fueled by air travel, cyberspace attacks, and cascading failures caused by the weakest link in a supply chain. Hence, a quantitative understanding of the mechanisms driving global network vulnerabilities is urgently needed. Developing methods for efficiently monitoring evolution of the global economy is essential to such understanding. Each year the World Economic Forum publishes an authoritative report on the state of the global economy and identifies risks that are likely to be active, impactful or contagious. Using a Cascading Alternating Renewal Process approach to model the dynamics of the global risk network, we are able to answer critical questions regarding the evolution of this network. To fully trace the evolution of the network we analyze the asymptotic state of risks (risk levels which would be reached in the long term if the risks were left unabated) given a snapshot in time; this elucidates the various challenges faced by the world community at each point in time. We also investigate the influence exerted by each risk on others. Results presented here are obtained through either quantitative analysis or computational simulations.

as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID parameters as individuals in genetic algorithm during the control process, and takes the integral function of absolute error control time as the optimization object to dynamically adjust the three PID control parameters, thus realize online optimization for PID control parameters. Simulation results show that the introduction of genetic algorithms for PID control system improves the dynamic performance, enhance system stability and operation speed, and get better control effect.

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