基于二步邻居拓扑的E-Burt结构洞检测算法
发布时间：2018-11-07 10:28
【摘要】：连接多个不同社团的节点称为结构洞节点,部分已有的结构洞节点检测方法虽然可以检测到关键节点,但存在一些不足:基于局部的测量方法忽略了网络拓扑结构;对于大规模复杂的网络来说,基于全局的测量方法可扩展性差,等等。为了高效准确地检测社会网络中具有影响力的节点,提出了一种新的结构洞度量方法 E-Burt,用来寻找结构洞节点。该方法利用节点与其二步邻居构成的拓扑关系来计算节点的有效规模,用该结果作为结构洞节点重要性的评价指标,计算每个节点的结构洞度量值,并给出了形式化定义。E-B算法基于网络拓扑结构,每次模拟迭代将选中的结构洞节点度量值置为零,下一次迭代只计算该节点二步邻居的有效规模,大大降低了时间复杂度。最后通过实验验证了算法的时间效率,分析了算法的精确度,对算法的正确性进行了证明,并与存在的经典结构洞发现算法进行了对比。
[Abstract]:Nodes connected with different communities are called structural hole nodes. Some of the existing structural hole node detection methods can detect key nodes, but there are some shortcomings: the local measurement method ignores the network topology; For large-scale and complex networks, global-based measurement methods have poor scalability, and so on. In order to efficiently and accurately detect the influential nodes in social networks, a new structural hole measurement method, E-Burt, is proposed to find structural holes. Based on the topological relation between nodes and their two-step neighbors, the effective scale of nodes is calculated. The result is used as the evaluation index of the importance of structural holes, and the structural hole weights of each node are calculated. The formal definition is given. E-B algorithm is based on the network topology. Each simulation iteration sets the selected structural hole node to zero, and the next iteration only calculates the effective scale of the two-step neighbor of the node. The time complexity is greatly reduced. Finally, the time efficiency of the algorithm is verified by experiments, the accuracy of the algorithm is analyzed, the correctness of the algorithm is proved, and the existence of the classical structure hole discovery algorithm is compared.
【作者单位】： 黑龙江大学计算机科学技术学院;黑龙江大学数据库与并行计算重点实验室;
【基金】：黑龙江大学研究生创新科研项目重点项目(YJSCX2016-018HLJU)
【分类号】：O157.5
本文编号：2316070
[Abstract]:Nodes connected with different communities are called structural hole nodes. Some of the existing structural hole node detection methods can detect key nodes, but there are some shortcomings: the local measurement method ignores the network topology; For large-scale and complex networks, global-based measurement methods have poor scalability, and so on. In order to efficiently and accurately detect the influential nodes in social networks, a new structural hole measurement method, E-Burt, is proposed to find structural holes. Based on the topological relation between nodes and their two-step neighbors, the effective scale of nodes is calculated. The result is used as the evaluation index of the importance of structural holes, and the structural hole weights of each node are calculated. The formal definition is given. E-B algorithm is based on the network topology. Each simulation iteration sets the selected structural hole node to zero, and the next iteration only calculates the effective scale of the two-step neighbor of the node. The time complexity is greatly reduced. Finally, the time efficiency of the algorithm is verified by experiments, the accuracy of the algorithm is analyzed, the correctness of the algorithm is proved, and the existence of the classical structure hole discovery algorithm is compared.
【作者单位】： 黑龙江大学计算机科学技术学院;黑龙江大学数据库与并行计算重点实验室;
【基金】：黑龙江大学研究生创新科研项目重点项目(YJSCX2016-018HLJU)
【分类号】：O157.5
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