In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for . Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times in computation of . To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.

中文翻译：

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基于新型节点中心度的定向网络有效经验鲁棒性评估

近年来，尽管已经提出并完成了对各种网络特性的广泛研究，但对于有向大规模在线社区网络中级联下的网络鲁棒性和节点脆弱性评估却几乎没有提出和进行。从本质上讲，在线定向社交网络是一个以群体为中心且以信息传播为主的在线平台，与传统的非定向社交网络截然不同。一些进一步的研究表明，在线社交网络对节点的随机删除具有很高的鲁棒性，但是对故意攻击却没有，特别是对于那些基于节点间度或基于节点有向系数的攻击。为了探索定向社交网络的鲁棒性，在本文中，我们提出了两个新的ITG节点中心性（基于信息传递增益的概率聚类系数）和（基于有向路径的节点重要性中心度）。这两个新的中心性模型旨在捕获定向在线社交网络中的这种级联效应。此外，我们还提出了一种基于的迭代的高效新计算方法。然后，通过对有向在线社交媒体和有向人类手机呼叫网络的合成签名网络和现实网络的大量实验，证明了我们的ITG和基于定向社交网络的鲁棒性和节点脆弱性评估方法比度和中间性等几种传统集中性方法更加准确，高效和快速。并且我们还提出了计算的迭代时间的可靠推理和证明过程。据我们所知，我们的研究为有向社交网络的鲁棒性提供了新的思路。