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Team formation in social networks based on local distance metric

机译:基于本地距离度量的社交网络团队形成

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The tremendous growth of social networking in last decades has resulted in an extensive research into this area. Social network users are enabled to easily communicate and collaborate with each other. Due to the significant role of network users, a new research topic in social networking analysis is team formation. The social network is modeled as a graph where nodes represent experts and edges show the initial collaborations between experts. The present research aims to tackle the problem to find an expert team in social network in order to complete a project that requires a set of skills. An algorithm is thus proposed to find the best suited team for the assigned project: a new method is devised as well to determine the distance between pairs of experts. Experimental results on DBLP dataset present the efficiency of our proposed method.
机译:过去几十年来,社交网络的迅猛发展导致对该领域的广泛研究。社交网络用户可以轻松地相互交流和协作。由于网络用户的重要作用,社交网络分析中的一个新研究主题是团队的形成。社交网络被建模为一个图形,其中节点代表专家,边表示专家之间的初始协作。本研究旨在解决该问题,以找到社交网络中的专家团队来完成需要一组技能的项目。因此,提出了一种算法,以找到最适合所分配项目的团队:还设计了一种新方法来确定专家对之间的距离。在DBLP数据集上的实验结果表明了我们提出的方法的有效性。

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