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The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.
相似文献Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.
相似文献In this paper, an algorithm is proposed for detection and joint estimation of parameters of multiple targets using rotating antenna array. This paper is sequel to our previous work addressing a two element antenna array only. Joint estimation of number of targets, the targets’ range, Doppler and their directions of arrivals is carried while the effects of antenna rotation are compensated for a multi element linear array. The effectiveness of the algorithm is verified by the simulation results carried out for an eight element array. The proposed algorithm is able to resolve targets with same range and Doppler. The computations of the proposed algorithm are calculated and comparison is also given with other algorithms.
相似文献In this paper, a framework of smart transportation system is proposed, aiming to address the transportation problem in Karachi city. In modern day world, the mega cities and urban areas are on the edge of transformation into smart cities. With the advancement of engineering and technology, smart cities are designed to integrate and utilize these scientific innovations to provide smart solutions and social innovations for sustainable infrastructure, thus they are able to provide its resident highest quality of life by utilizing its resources effectively. One of the major application of smart cities is the Smart Transportation System, which provides safer, quick, environment friendly service to the residents. Thus, this study highlights the current traffic situation of Karachi and propose a framework to transform it into a smart transportation system. In order to have a smart transportation system, it is necessary to have in-depth knowledge and information about the city dynamics and its traffic related issues. Therefore, this study also highlights current traffic situation of Karachi, its road conditions and capacity, vehicles condition, alternate mean of transport (other than road-based system) and its present condition, and finally proposes a framework to develop a smart transportation system while keeping in mind the aforesaid traffic problems.
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