Structural hole spanners play key role in information diffusion, community detection, epidemic diseases and rumors spreading, link prediction and viral marketing, the discovery for them is a key research work in the area of social networks. Some scholars have proposed inspired models and methods based on Mathematics, Sociology, and Economics. In this paper, we try to give a more visual and detailed definition of structural hole spanner based on the existing work, and propose a novel algorithm to identify structural hole spanner based on community forest model and diminishing marginal utility. Our work includes following four folds. Firstly we revealed the diminishing marginal utility phenomenon in the process of community reconstruction. Secondly we proved that metrics based on local or one-sided features can not be used as a criterion for judging structural hole spanner. Thirdly we proved that the influence of SHS is not related with the distribution of SHS in the network. Fourthly we develop a novel algorithm to identify SHS. Our algorithm has slightly better performance than the state-of-the-art algorithms. It worked well on Zachary’s karate club, American College Football, ground-truth samples sampled from DBLP, ground-truth samples sampled from Youtube and large-scale collaboration network DBLP.

结构性空洞扳手在信息传播，社区发现，流行病和谣言传播，链接预测和病毒营销中起着关键作用，对他们的发现是社交网络领域的关键研究工作。一些学者基于数学，社会学和经济学提出了启发性的模型和方法。本文在现有工作的基础上，试图给出更加直观，详细的结构孔扳手定义，并提出了一种基于社区森林模型和递减边际效用的结构孔扳手识别算法。我们的工作包括以下四个方面。首先，我们揭示了社区重建过程中边际效用逐渐减少的现象。其次，我们证明了基于局部或单侧特征的度量不能用作判断结构孔扳手的标准。第三，我们证明了SHS的影响与网络中SHS的分布无关。第四，我们开发了一种新颖的算法来识别SHS。我们的算法比最新的算法具有更好的性能。它在Zachary的空手道俱乐部，美国大学橄榄球，从DBLP采样的真实样本，从Youtube采样的真实样本以及大型协作网络DBLP上都运行良好。