MODELING SOCIAL NETWORKS FROM SAMPLED DATA.
MODELING SOCIAL NETWORKS FROM SAMPLED DATA.
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DOI：
10.1214/08-aoas221
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Gile KJ
中科院分区：
文献类型：
--
作者：
Handcock MS;Gile KJ
网络模型被广泛用于表示交互单元之间的关系信息以及这些关系的结构含义。近年来，社会网络研究将大量注意力集中在网络的随机图模型上，这些网络的节点代表单个社会参与者，其边代表参与者之间的特定关系。社交网络模型的大多数推断都假设观察到所有可能链接的存在或不存在，信息是完全可靠的，并且不存在测量(例如，记录)错误。这在实践中显然是不正确的，因为许多网络数据是通过抽样调查收集的。此外，即使试图进行人口普查，也会遗漏个人和个人之间的联系(即，没有出现在记录的数据中)。在本文中，我们发展了基于采样网络信息的推理的概念和计算理论。我们首先回顾实践中使用的网络抽样设计的形式。我们考虑从可能性框架进行推理，并开发出一种反映其在该框架内的处理的网络数据类型。然后，我们根据来自自适应网络设计的信息，对社会网络模型进行推理。我们通过分析链接跟踪抽样设计对协作网络的影响来激发和说明这些想法。
Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.