The popularity of smart phones has greatly promoted the development of mobile advertising, especially the development of mobile application (app) advertising. An ad network as a medium between... Show More

The popularity of smart phones has greatly promoted the development of mobile advertising, especially the development of mobile application (app) advertising. An ad network as a medium between advertisers and app developers can not only provide advertising services for advertisers, but also bring app developers incomes. Existing forms of advertising partnership between advertisers and advertising networks (ad networks) are generally centered on ad networks. However, the advertising-centric cooperation model sometimes cannot meet the specialized advertising needs of advertisers, and the advertisers have to spend more to find the suitable partners. In this paper, the authors consider the advertising problem from the perspective of advertisers. They design an advertiser-centric cooperative mechanism to help an advertiser to attract cooperation from ad networks. They formulate the advertiser-centric cooperative problem into a noncooperative game between an advertiser and a number of ad networks. It is proved that a unique Stackelberg equilibrium exists in the game and the authors have calculated the Nash equilibrium for each ad network. An algorithm that can achieve the unique equilibrium point is proposed. Extensive experiments' results are also conducted to verify the performance of the proposed mechanism in terms of the investment related factors.

In this paper, the authors try to systematically investigate the problem of individual doctor recommendation and propose a novel method to enable patients to access such intelligent medical service.... Show More

In this paper, the authors try to systematically investigate the problem of individual doctor recommendation and propose a novel method to enable patients to access such intelligent medical service. In their method, the authors first mine doctor-patient ties/relationships via Time-constraint Probability Factor Graph model (TPFG) from a medical social network. Next, they design a constraint-based optimization framework to efficiently improve the accuracy for doctor-patient relationship mining. Last, they propose a novel Individual Doctor Recommendation Model, namely IDR-Model, to compute doctor recommendation success rate based on weighted average method. The authors conduct experiments to verify the method on a real medical data set. Experimental results show that they obtain better accuracy of mining doctor-patient relationship from the network, and doctor recommendation results of IDR-Model are reasonable and satisfactory.

A significant trend caused by cloud computing is to aggregate applications for sharing resources. Thus, it is necessary to provide fair resources and performance among applications, especially for... Show More

A significant trend caused by cloud computing is to aggregate applications for sharing resources. Thus, it is necessary to provide fair resources and performance among applications, especially for the network, which is provided in the best-effort manner in current clouds. Although many studies have made efforts for provisioning fair bandwidth, they are not sufficient for network fairness. In fact, for interactive applications, response time is more sensitive than bandwidth, and users expect a fair response time not just bandwidth. In this study, the authors want to investigate whether the traditional methods of sharing bandwidth can help the fairness of response time. They show that: (1) bandwidth has little relationship to response time, and adjusting bandwidth hardly affects response time in most cases. Thus, the traditional methods cannot help the fairness of response time much; and (2) the fairness between components is different from the one between transactions, and many prior studies only consider the former while ignoring the latter. Thus, the authors cannot help much for multitier applications consisting of multiple transactions either. As a result, they construct a model with two metrics to evaluate the fairness status of the network sharing, while considering the applications' characteristics on both the response time and throughput. Based on the model, they also propose a mechanism to improve the fairness status. The evaluation results show that the authors' mechanism improves the fairness status by 26.5%–52.8%, and avoids performance degradation compared to some practical mechanisms.

This article focused on seeking a new heuristic algorithm for the influence maximization problem in complex social networks, in which a small subset of individuals are intentionally selected as... Show More

This article focused on seeking a new heuristic algorithm for the influence maximization problem in complex social networks, in which a small subset of individuals are intentionally selected as seeds to trigger a large cascade of further adoptions of a new behavior under certain influence cascade models. In literature, degree and other centrality-based heuristics are commonly used to estimate the influential power of individuals in social networks. The major issues with degree-based heuristics are twofold. First, those results are only derived for the uniform IC model, in which propagation probabilities on all social links are set as same, which is rarely the case in reality; Second, intuitively, an individual's influence power depends not only on the number of direct friends, but also relates to kinds of those friends, that is, the neighbors' influence should also be taken into account when measuring one's influential power. Based on the general weighted cascade model (WC), this article proposes Pagerank-inspired heuristic scheme, PRDiscount, which explicitly discounts the influence power of those individuals who have social relationships with chosen seeds, to alleviate the “overlapping effect” occurred in behavior diffusion. Then, the authors use both the artificially constructed social network graphs (with the features of power-law degree distribution and small-world characteristics) and the real-data traces of social networks to verify the performance of their proposal. Simulations illustrate that PRDiscount can advantage over the existing degree-based discount algorithm, DegreeDiscount, and achieve the comparable performance as greedy algorithm.