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Structural properties and generative model of non-giant connected components in social networks



Most previous studies have mainly focused on the analyses of one entire network (graph) or the giant connected components of networks. In this paper, we investigate the disconnected components (non-giant connected component) of some real social networks, and report some interesting discoveries about structural properties of disconnected components. We study three diverse, real networks and compute the significance profile of each component. We discover some similarities in the local structure between the giant connected component and disconnected components in diverse social networks. Then we discuss how to detect network attacks based on the local structure properties of networks. Furthermore, we propose an empirical generative model called iFriends to generate networks that follow our observed patterns.


前人对社交网络结构的研究往往关注于网络整体或者网络中的极大连通分量。在本文中, 基于真实的社交网络数据, 我们研究了社交网络中非极大连通分量的结构特征, 并发现了一些有趣的规律。对每个网络, 我们对每个连通分量计算其重要性剖面(Significance Profile)。我们发现在这些网络中, 极大连通分量和非极大连通分量之间存在着结构上的相似性。基于这个发现, 我们进一步利用网络的结构特征以检测网络攻击。最后, 我们提出了一个网络生成模型, 这个模型生成的网络能够观察到我们在这篇论文中发现的规律。

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Correspondence to Jianwei Niu.

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Niu, J., Wang, L. Structural properties and generative model of non-giant connected components in social networks. Sci. China Inf. Sci. 59, 123101 (2016).

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  • disconnected components
  • giant connected component
  • structural properties
  • significance profile
  • generative model


  • 极大连通分量
  • 非极大连通分量
  • 结构特征
  • 重要性剖面
  • 网络生成模型