An influence maximization method based on crowd emotion under an emotion-based attribute social network

Most research on influence maximization focuses on the network structure features of the diffusion process but lacks the consideration of multi-dimensional characteristics. This paper proposes the attributed influence maximization based on the crowd emotion, aiming to apply the user’s emotion and group features to study the influence of multi-dimensional characteristics on information propagation. To measure the interaction effects of individual emotions, we define the user emotion power and the cluster credibility, and propose a potential influence user discovery algorithm based on the emotion aggregation mechanism to locate seed candidate sets. A two-factor information propagation model is then introduced, which considers the complexity of real networks. Experiments on real-world datasets demonstrate the effectiveness of the proposed algorithm. The results outperform the heuristic methods and are almost consistent with the greedy methods yet with improved time performance.     

Exploring the effect of dynamic seed activation in social networks

In this paper, we address the problem of maximizing the influence in a social network by hiring a few users in the network to propagate the information. Considering limited budget and time, hired users (seeds) are activated dynamically at different time intervals over a time horizon. This motivates to avoid the same seed activation in consecutive time intervals that leads to deteriorating the seeds’ efficiency. The aim of this paper is to maximize the total gain obtained in the process of maximizing the influence in a social network. Total gain is obtained by earning of influencing the customers and then deducting the cost incurred for influencing the users. Therefore, an improvised memetic algorithm is developed to find the seeds that are to be activated at different time intervals to maximize the gain. Experimental results validate the effectiveness of the proposed algorithm, and it is found to perform better in identifying the potential seeds with minimum expenditure.     

Mining social influence in science and vice-versa: A topic correlation approach

There is no doubt that scientific discoveries have always brought changes to society. New technologies help solve social problems such as transportation and education, while research brings benefits such as curing diseases and improving food production. Despite the impacts caused by science and society on each other, this relationship is rarely studied and they are often seen as different universes. Previous literature focuses only on a single domain, detecting social demands or research fronts for example, without ever crossing the results for new insights. In this work, we create a system that is able to assess the relationship between social and scholar data using the topics discussed in social networks and research topics. We use the articles as science sensors and humans as social sensors via social networks. Topic modeling algorithms are used to extract and label social subjects and research themes and then topic correlation metrics are used to create links between them if they have a significant relationship. The proposed system is based on topic modeling, labeling and correlation from heterogeneous sources, so it can be used in a variety of scenarios. We make an evaluation of the approach using a large-scale Twitter corpus combined with a PubMed article corpus. In both of them, we work with data of the Zika epidemic in the world, as this scenario provides topics and discussions on both domains. Our work was capable of discovering links between various topics of different domains, which suggests that some of the relationships can be automatically inferred by the sensors. Results can open new opportunities for forecasting social behavior, assess community interest in a scientific subject or directing research to the population welfare.     

无线中继网络的联合路由与调度的近似最优算法

在基于802.16j的无线中继网络中,考虑路由和调度的联合优化问题,最小化系统总调度时间. 首先采用线性规划的方法建立路由,进行链路业务速率分配,然后基于平移和交换思想提出一种链路调度算法. 理论分析证明所提算法的性能在最坏情况下,不会超过最优性能的1.5倍. 仿真结果表明,所提算法的平均性能非常接近最优性能.     

对计算水平分层介质响应正交归一化法的改进

汪荣江提出一个简单的正交归一化技术来克服经典的Thomson-Haskell传播矩阵方法中存在的数值不稳定问题.为了进一步提高计算效率,给出该方法的2种改进.一种改进方法是将传播矩阵中与频率无关的部分分离出来,对于某一固定的水平慢度,这些矩阵只需计算一次;另一个改进是利用Langer块对角化的技术,将传播矩阵分解为几个稀疏矩阵的乘积.我们将改进之后的算法应用于计算水平分层模型中的广义反射系数.较之原有方案,提出的改进能节省一半计算时间.     

全球制造网络中后发企业的持续升级:一个双元性视角

全球制造网络"嵌入悖论"的存在使后发企业面临沿价值链持续攀升的难题,即全球制造网络要求后发企业专注于价值链制造环节内的工艺升级与产品升级以提升当前经营效率的同时,也可能阻碍后发企业职能升级与跨链升级导致其长期适应能力减退。本研究通过对我国大型民营企业海天集团持续升级的纵向单案例研究,揭示了双元性可以作为后发企业破解全球制造网络"嵌入悖论"的一种机制,并进一步识别了联结多样化在构建双元性中的重要作用。因此,对于在全球制造网络中要实现持续升级的后发企业,首先必须要有主动构建双元性的战略意图,其次可在合作伙伴、合作方式与合作职能三个维度之间进行联结多样化配置来构建双元性。