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Efficient Algorithms for the Identification of Top-k Structural Hole Spanners in Large Social Networks

机译:大型社交网络中识别前k个结构孔扳手的高效算法

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Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as structural hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the structural hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top-k structural hole spanner problem. We first provide a novel model to measure the quality of structural hole spanners through exploiting the structural hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the structural hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast.
机译:最近的研究表明,社交网络中的个人可以分为紧密连接的社区的不同组,这些桥接不同社区的人(称为结构漏洞扳手)具有从社区获取资源/信息的巨大潜力,因此可以从社区中受益。访问。结构孔扳手在许多实际应用中至关重要,例如社区检测,扩散控制,病毒营销等。尽管它们很重要,却很少引起人们的注意。特别地,如何准确地表征结构孔扳手以及如何设计有效而可扩展的算法以在大型社交网络中找到它们是基本问题。在本文中,我们研究了top-k结构孔扳手问题。我们首先提供一种新颖的模型,通过利用结构孔扳手的性能来测量结构孔扳手的质量。由于其NP硬度,我们然后通过开发创新的过滤技术来设计出两种有效但可扩展的算法,这些技术可以尽快滤除不太可能的解决方案,而所提出的技术则建立在快速估计上下限的基础上。最佳解决方案的成本,并在真实的社交网络中利用铰接点。我们最终进行了广泛的实验,以验证所提出模型的有效性,并使用现实世界的数据集评估所提出算法的性能。实验结果表明,所提出的模型能够准确地捕获结构孔扳手的特征,在所有考虑的社交网络中,所提出的算法所发现的结构孔扳手都比现有算法要好得多。很快

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